def prep_bbox(sess, x, y, X_train, Y_train, X_test, Y_test, nb_epochs,
batch_size, learning_rate):
"""
Define and train a model that simulates the "remote"
black-box oracle described in the original paper.
:param sess: the TF session
:param x: the input placeholder for MNIST
:param y: the ouput placeholder for MNIST
:param X_train: the training data for the oracle
:param Y_train: the training labels for the oracle
:param X_test: the testing data for the oracle
:param Y_test: the testing labels for the oracle
:param nb_epochs: number of epochs to train model
:param batch_size: size of training batches
:param learning_rate: learning rate for training
:return:
"""
# Define TF model graph (for the black-box model)
if DATASET == "mnist":
model = MNISTModel(use_log=True).model
else:
model = CIFARModel(use_log=True).model
predictions = model(x)
print("Defined TensorFlow model graph.")
# Train an MNIST model
if FLAGS.load_pretrain:
tf_model_load(sess)
else:
train_params = {
'nb_epochs': nb_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate
}
model_train(sess,
x,
y,
predictions,
X_train,
Y_train,
verbose=True,
save=True,
args=train_params)
# Print out the accuracy on legitimate data
eval_params = {'batch_size': batch_size}
accuracy = model_eval(sess,
x,
y,
predictions,
X_test,
Y_test,
args=eval_params)
print('Test accuracy of black-box on legitimate test '
'examples: ' + str(accuracy))
return model, predictions, accuracy
def checkpoint_load(sess, checkpoint_dir, moving_variables=None):
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
if moving_variables:
variable_averages = tf.train.ExponentialMovingAverage(0.9)
variables_to_restore = variable_averages.variables_to_restore(moving_variables)
saver = tf.train.Saver(variables_to_restore)
saver.restore(sess, ckpt.model_checkpoint_path)
print(sess.run(moving_variables))
tf_model_load(sess, ckpt.model_checkpoint_path)
return True
print('restore fails: please provide correct checkpoint directory')
return False
def checkpoint_load(sess, checkpoint_dir, moving_variables=None):
ckpt = tf.train.get_checkpoint_state(checkpoint_dir)
if ckpt and ckpt.model_checkpoint_path:
if moving_variables:
variable_averages = tf.train.ExponentialMovingAverage(0.9)
variables_to_restore = variable_averages.variables_to_restore(
moving_variables)
saver = tf.train.Saver(variables_to_restore)
saver.restore(sess, ckpt.model_checkpoint_path)
print(sess.run(moving_variables))
tf_model_load(sess, ckpt.model_checkpoint_path)
return True
print('restore fails: please provide correct checkpoint directory')
return False
def __test():
# report = AccuracyReport()
tf.set_random_seed(1234)
sess = tf.Session()
set_log_level(logging.DEBUG)
# Get MNIST test data
mnist = MNIST(train_start=0, train_end=60000, test_start=0, test_end=10000)
x_train, y_train = mnist.get_set('train')
x_test, y_test = mnist.get_set('test')
# Obtain Image Parameters
img_rows, img_cols, nchannels = x_train.shape[1:4]
nb_classes = y_train.shape[1]
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, nchannels))
y = tf.placeholder(tf.float32, shape=(None, nb_classes))
nb_filters = 64
# Define TF model graph
model = ModelBasicCNN('model1', nb_classes, nb_filters)
preds = model.get_logits(x)
loss = CrossEntropy(model, smoothing=0.1)
print("Defined TensorFlow model graph.")
# Train an MNIST model
train_params = {
'nb_epochs': NB_EPOCHS,
'batch_size': BATCH_SIZE,
'learning_rate': LEARNING_RATE,
'filename': os.path.split(MODEL_PATH)[-1]
}
rng = np.random.RandomState([2017, 8, 30])
# check if we've trained before, and if we have, use that pre-trained model
if os.path.exists(model_path + ".meta"):
tf_model_load(sess, model_path)
else:
train(sess, loss, x_train, y_train, args=train_params, rng=rng)
saver = tf.train.Saver()
saver.save(sess, model_path)
# Evaluate the accuracy of the MNIST model on legitimate test examples
eval_params = {'batch_size': BATCH_SIZE}
accuracy = model_eval(sess, x, y, preds, x_test, y_test, args=eval_params)
assert x_test.shape[0] == test_end - test_start, x_test.shape
print('Test accuracy on legitimate test examples: {0}'.format(accuracy))
def cifar10_tutorial(train_start=0,
train_end=60000,
test_start=0,
test_end=10000,
nb_epochs=NB_EPOCHS,
batch_size=BATCH_SIZE,
model_path=MODEL_PATH,
learning_rate=LEARNING_RATE,
clean_train=CLEAN_TRAIN,
testing=False,
backprop_through_attack=BACKPROP_THROUGH_ATTACK,
nb_filters=NB_FILTERS,
num_threads=None,
label_smoothing=0.1):
"""
CIFAR10 cleverhans tutorial
:param train_start: index of first training set example
:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
:param nb_epochs: number of epochs to train model
:param batch_size: size of training batches
:param learning_rate: learning rate for training
:param clean_train: perform normal training on clean examples only
before performing adversarial training.
:param testing: if true, complete an AccuracyReport for unit tests
to verify that performance is adequate
:param backprop_through_attack: If True, backprop through adversarial
example construction process during
adversarial training.
:param label_smoothing: float, amount of label smoothing for cross entropy
:return: an AccuracyReport object
"""
# Object used to keep track of (and return) key accuracies
report = AccuracyReport()
# Set TF random seed to improve reproducibility
tf.set_random_seed(1234)
# Set logging level to see debug information
set_log_level(logging.DEBUG)
# Create TF session
if num_threads:
config_args = dict(intra_op_parallelism_threads=1)
else:
config_args = {}
sess = tf.Session(config=tf.ConfigProto(**config_args))
# Get CIFAR10 data
data = CIFAR10(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end)
dataset_size = data.x_train.shape[0]
dataset_train = data.to_tensorflow()[0]
dataset_train = dataset_train.map(
lambda x, y: (random_shift(random_horizontal_flip(x)), y), 4)
dataset_train = dataset_train.batch(batch_size)
dataset_train = dataset_train.prefetch(16)
x_train, y_train = data.get_set('train')
x_test, y_test = data.get_set('test')
# Use Image Parameters
img_rows, img_cols, nchannels = x_test.shape[1:4]
nb_classes = y_test.shape[1]
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, nchannels))
y = tf.placeholder(tf.float32, shape=(None, nb_classes))
# Train an MNIST model
train_params = {
'nb_epochs': nb_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate,
'filename': os.path.split(model_path)[-1]
}
eval_params = {'batch_size': batch_size}
rng = np.random.RandomState([2017, 8, 30])
def do_eval(preds, x_set, y_set, report_key, is_adv=None):
acc = model_eval(sess, x, y, preds, x_set, y_set, args=eval_params)
setattr(report, report_key, acc)
if is_adv is None:
report_text = None
elif is_adv:
report_text = 'adversarial'
else:
report_text = 'legitimate'
if report_text:
print('Test accuracy on %s examples: %0.4f' % (report_text, acc))
if clean_train:
print('start')
#model = CNN('model1', nb_classes, isL2 = True)
model = make_wresnet(scope='model1')
preds = model.get_logits(x)
loss = CrossEntropy(model, smoothing=label_smoothing)
tf_model_load(
sess,
'/nfs/nas4/data-hanwei/data-hanwei/DATA/models/wresnet/cifar1')
def evaluate():
do_eval(preds, x_test, y_test, 'clean_train_clean_eval', False)
optimizer = tf.train.MomentumOptimizer(learning_rate=0.0008,
momentum=0)
#optimizer = tf.train.MomentumOptimizer(learning_rate=0.0008,momentum=0.9)
#optimizer = tf.train.MomentumOptimizer(learning_rate=0.001,momentum=0.9)
train(sess,
x,
y,
model,
None,
None,
dataset_train=dataset_train,
dataset_size=dataset_size,
evaluate=evaluate,
args=train_params,
rng=rng,
var_list=model.get_params(),
optimizer=optimizer)
saver = tf.train.Saver()
saver.save(sess, model_path)
# Calculate training error
if testing:
do_eval(preds, x_train, y_train, 'train_clean_train_clean_eval')
return report
def mnist_tutorial_cw(train_start=0, train_end=60000, test_start=0,
test_end=10000, viz_enabled=False, nb_epochs=6,
batch_size=128, nb_classes=10, source_samples=1,
learning_rate=0.001, attack_iterations=100,
model_path=os.path.join("models", "mnist"),
targeted=True):
"""
MNIST tutorial for Carlini and Wagner's attack
:param train_start: index of first training set example
:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
:param viz_enabled: (boolean) activate plots of adversarial examples
:param nb_epochs: number of epochs to train model
:param batch_size: size of training batches
:param nb_classes: number of output classes
:param source_samples: number of test inputs to attack
:param learning_rate: learning rate for training
:param model_path: path to the model file
:param targeted: should we run a targeted attack? or untargeted?
:return: an AccuracyReport object
"""
# Object used to keep track of (and return) key accuracies
report = AccuracyReport()
# MNIST-specific dimensions
img_rows = 28
img_cols = 28
channels = 1
# Set TF random seed to improve reproducibility
#tf.set_random_seed(1234)
# Create TF session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
#config.log_device_placement=True
sess = tf.Session(config=config)
print("Created TensorFlow session.")
set_log_level(logging.DEBUG)
# Get MNIST test data
X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end)
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, channels))
y = tf.placeholder(tf.float32, shape=(None, nb_classes))
# Define TF model graph
model = make_basic_cnn()
preds = model(x)
print("Defined TensorFlow model graph.")
###########################################################################
# Training the model using TensorFlow
###########################################################################
train_params = {
'nb_epochs': nb_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate,
'train_dir': os.path.join(*os.path.split(model_path)[:-1]),
'filename': os.path.split(model_path)[-1]
}
rng = np.random.RandomState([2017, 8, 30])
# check if we've trained before, and if we have, use that pre-trained model
if os.path.exists(model_path + ".meta"):
tf_model_load(sess, model_path)
else:
model_train(sess, x, y, preds, X_train, Y_train, args=train_params,
save=os.path.exists("models"), rng=rng)
# Evaluate the accuracy of the MNIST model on legitimate test examples
eval_params = {'batch_size': batch_size}
accuracy = model_eval(sess, x, y, preds, X_test, Y_test, args=eval_params)
assert X_test.shape[0] == test_end - test_start, X_test.shape
print('Test accuracy on legitimate test examples: {0}'.format(accuracy))
report.clean_train_clean_eval = accuracy
###########################################################################
# Craft adversarial examples using Carlini and Wagner's approach
###########################################################################
nb_adv_per_sample = str(nb_classes - 1) if targeted else '1'
# Instantiate a CW attack object
cw = CarliniWagnerL2(model, back='tf', sess=sess)
fgsm = FastGradientMethod(model, sess=sess)
result = np.zeros((5,len(X_test)))
strength = np.zeros((3,len(X_test)))
adv_ys = None
yname = "y"
cw_params = {'binary_search_steps': 1,
'max_iterations': attack_iterations,
'learning_rate': 0.1,
'batch_size': source_samples,
'initial_const': 10}
fgsm_eps = [0.1,0.3, 0.5]
for j in fgsm_eps:
fgsm_params = {'eps': j,
'clip_min': 0.,
'clip_max': 1.}
for i in range(len(X_test)):
feed_dict = {x: X_test[i].reshape((1,28,28,1))}
Classes0 = preds.eval(feed_dict=feed_dict,session=sess)
Class0 = np.argmax(Classes0)
result[0,i] = Class0
adv_inputs = X_test[i]
adv_inputs = adv_inputs.reshape((1,28,28,1))
#adv = cw.generate_np(adv_inputs,**cw_params)
adv = fgsm.generate_np(adv_inputs, **fgsm_params)
pdb.set_trace()
feed_dict = {x: adv}
Classes1 = preds.eval(feed_dict=feed_dict,session=sess)
Class1 = np.argmax(Classes1)
result[1,i] = Class1
# Compute the average distortion introduced by the algorithm
percent_perturbed = np.mean(np.sum((adv - adv_inputs)**2,
axis=(1, 2, 3))**.5)
strength[0,i] = percent_perturbed
adv2 = cw.generate_np(adv,**cw_params)
feed_dict = {x: adv2}
Classes2 = preds.eval(feed_dict=feed_dict,session=sess)
Class2 = np.argmax(Classes2)
result[2,i] = Class2
# Compute the average distortion introduced by the algorithm
percent_perturbed2 = np.mean(np.sum((adv2 - adv)**2,
axis=(1, 2, 3))**.5)
strength[1,i] = percent_perturbed2
adv_f = sig.medfilt(adv,(1,3,3,1))
feed_dict = {x: adv_f}
Classes1 = preds.eval(feed_dict=feed_dict,session=sess)
Class1 = np.argmax(Classes1)
result[3,i] = Class1
# Compute the average distortion introduced by the algorithm
#percent_perturbed = np.mean(np.sum((adv - adv_inputs)**2,
# axis=(1, 2, 3))**.5)
#strength[0,i] = percent_perturbed
adv2_f = cw.generate_np(adv_f,**cw_params)
feed_dict = {x: adv2_f}
Classes2 = preds.eval(feed_dict=feed_dict,session=sess)
Class2 = np.argmax(Classes2)
result[4,i] = Class2
# Compute the average distortion introduced by the algorithm
percent_perturbed2 = np.mean(np.sum((adv2_f - adv_f)**2,
axis=(1, 2, 3))**.5)
strength[2,i] = percent_perturbed2
if i%100 == 0:
print(i)
# exit()
# Close TF session
sess.close()
sio.savemat('fgsm_mnist.mat',{'adv_01':adv_01,'adv_03':adv_03, 'adv_05':adv_05 'strength':strength})
def mnist_tutorial_cw(train_start=0, train_end=60000, test_start=0,
test_end=10000, viz_enabled=True, nb_epochs=6,
batch_size=128, source_samples=10,
learning_rate=0.001, attack_iterations=100,
model_path=os.path.join("models", "mnist"),
targeted=True):
"""
MNIST tutorial for Carlini and Wagner's attack
:param train_start: index of first training set example
:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
:param viz_enabled: (boolean) activate plots of adversarial examples
:param nb_epochs: number of epochs to train model
:param batch_size: size of training batches
:param nb_classes: number of output classes
:param source_samples: number of test inputs to attack
:param learning_rate: learning rate for training
:param model_path: path to the model file
:param targeted: should we run a targeted attack? or untargeted?
:return: an AccuracyReport object
"""
# Object used to keep track of (and return) key accuracies
report = AccuracyReport()
# Set TF random seed to improve reproducibility
tf.set_random_seed(1234)
# Create TF session
sess = tf.Session()
print("Created TensorFlow session.")
set_log_level(logging.DEBUG)
# Get MNIST test data
x_train, y_train, x_test, y_test = data_mnist(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end)
# Obtain Image Parameters
img_rows, img_cols, nchannels = x_train.shape[1:4]
nb_classes = y_train.shape[1]
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols,
nchannels))
y = tf.placeholder(tf.float32, shape=(None, nb_classes))
nb_filters = 64
# Define TF model graph
model = ModelBasicCNN('model1', nb_classes, nb_filters)
preds = model.get_logits(x)
loss = LossCrossEntropy(model, smoothing=0.1)
print("Defined TensorFlow model graph.")
###########################################################################
# Training the model using TensorFlow
###########################################################################
# Train an MNIST model
train_params = {
'nb_epochs': nb_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate,
'train_dir': os.path.join(*os.path.split(model_path)[:-1]),
'filename': os.path.split(model_path)[-1]
}
rng = np.random.RandomState([2017, 8, 30])
# check if we've trained before, and if we have, use that pre-trained model
if os.path.exists(model_path + ".meta"):
tf_model_load(sess, model_path)
else:
train(sess, loss, x, y, x_train, y_train, args=train_params,
save=os.path.exists("models"), rng=rng)
# Evaluate the accuracy of the MNIST model on legitimate test examples
eval_params = {'batch_size': batch_size}
accuracy = model_eval(sess, x, y, preds, x_test, y_test, args=eval_params)
assert x_test.shape[0] == test_end - test_start, x_test.shape
print('Test accuracy on legitimate test examples: {0}'.format(accuracy))
report.clean_train_clean_eval = accuracy
###########################################################################
# Craft adversarial examples using Carlini and Wagner's approach
###########################################################################
nb_adv_per_sample = str(nb_classes - 1) if targeted else '1'
print('Crafting ' + str(source_samples) + ' * ' + nb_adv_per_sample +
' adversarial examples')
print("This could take some time ...")
# Instantiate a CW attack object
cw = CarliniWagnerL2(model, back='tf', sess=sess)
if viz_enabled:
assert source_samples == nb_classes
idxs = [np.where(np.argmax(y_test, axis=1) == i)[0][0]
for i in range(nb_classes)]
if targeted:
if viz_enabled:
# Initialize our array for grid visualization
grid_shape = (nb_classes, nb_classes, img_rows, img_cols,
nchannels)
grid_viz_data = np.zeros(grid_shape, dtype='f')
adv_inputs = np.array(
[[instance] * nb_classes for instance in x_test[idxs]],
dtype=np.float32)
else:
adv_inputs = np.array(
[[instance] * nb_classes for
instance in x_test[:source_samples]], dtype=np.float32)
one_hot = np.zeros((nb_classes, nb_classes))
one_hot[np.arange(nb_classes), np.arange(nb_classes)] = 1
adv_inputs = adv_inputs.reshape(
(source_samples * nb_classes, img_rows, img_cols, nchannels))
adv_ys = np.array([one_hot] * source_samples,
dtype=np.float32).reshape((source_samples *
nb_classes, nb_classes))
yname = "y_target"
else:
if viz_enabled:
# Initialize our array for grid visualization
grid_shape = (nb_classes, 2, img_rows, img_cols, nchannels)
grid_viz_data = np.zeros(grid_shape, dtype='f')
adv_inputs = x_test[idxs]
else:
adv_inputs = x_test[:source_samples]
adv_ys = None
yname = "y"
cw_params = {'binary_search_steps': 1,
yname: adv_ys,
'max_iterations': attack_iterations,
'learning_rate': 0.1,
'batch_size': source_samples * nb_classes if
targeted else source_samples,
'initial_const': 10}
adv = cw.generate_np(adv_inputs,
**cw_params)
eval_params = {'batch_size': np.minimum(nb_classes, source_samples)}
if targeted:
adv_accuracy = model_eval(
sess, x, y, preds, adv, adv_ys, args=eval_params)
else:
if viz_enabled:
adv_accuracy = 1 - \
model_eval(sess, x, y, preds, adv, y_test[
idxs], args=eval_params)
else:
adv_accuracy = 1 - \
model_eval(sess, x, y, preds, adv, y_test[
:source_samples], args=eval_params)
if viz_enabled:
for j in range(nb_classes):
if targeted:
for i in range(nb_classes):
grid_viz_data[i, j] = adv[i * nb_classes + j]
else:
grid_viz_data[j, 0] = adv_inputs[j]
grid_viz_data[j, 1] = adv[j]
print(grid_viz_data.shape)
print('--------------------------------------')
# Compute the number of adversarial examples that were successfully found
print('Avg. rate of successful adv. examples {0:.4f}'.format(adv_accuracy))
report.clean_train_adv_eval = 1. - adv_accuracy
# Compute the average distortion introduced by the algorithm
percent_perturbed = np.mean(np.sum((adv - adv_inputs)**2,
axis=(1, 2, 3))**.5)
print('Avg. L_2 norm of perturbations {0:.4f}'.format(percent_perturbed))
# Close TF session
sess.close()
# Finally, block & display a grid of all the adversarial examples
if viz_enabled:
import matplotlib.pyplot as plt
_ = grid_visual(grid_viz_data)
return report
def mnist_tutorial_cw(
train_start=0,
train_end=60000,
test_start=0,
test_end=10000,
viz_enabled=VIZ_ENABLED,
nb_epochs=NB_EPOCHS,
batch_size=BATCH_SIZE,
source_samples=SOURCE_SAMPLES,
learning_rate=LEARNING_RATE,
attack_iterations=ATTACK_ITERATIONS,
model_path=MODEL_PATH,
targeted=TARGETED,
):
"""
MNIST tutorial for Carlini and Wagner's attack
:param train_start: index of first training set example
:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
:param viz_enabled: (boolean) activate plots of adversarial examples
:param nb_epochs: number of epochs to train model
:param batch_size: size of training batches
:param nb_classes: number of output classes
:param source_samples: number of test inputs to attack
:param learning_rate: learning rate for training
:param model_path: path to the model file
:param targeted: should we run a targeted attack? or untargeted?
:return: an AccuracyReport object
"""
# Object used to keep track of (and return) key accuracies
report = AccuracyReport()
# Set TF random seed to improve reproducibility
tf.set_random_seed(1234)
# Create TF session
sess = tf.Session()
print("Created TensorFlow session.")
set_log_level(logging.DEBUG)
# Get MNIST test data
mnist = MNIST(
train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end,
)
x_train, y_train = mnist.get_set("train")
x_test, y_test = mnist.get_set("test")
# Obtain Image Parameters
img_rows, img_cols, nchannels = x_train.shape[1:4]
nb_classes = y_train.shape[1]
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, nchannels))
y = tf.placeholder(tf.float32, shape=(None, nb_classes))
nb_filters = 64
# Define TF model graph
model = ModelBasicCNN("model1", nb_classes, nb_filters)
preds = model.get_logits(x)
loss = CrossEntropy(model, smoothing=0.1)
print("Defined TensorFlow model graph.")
###########################################################################
# Training the model using TensorFlow
###########################################################################
# Train an MNIST model
train_params = {
"nb_epochs": nb_epochs,
"batch_size": batch_size,
"learning_rate": learning_rate,
"filename": os.path.split(model_path)[-1],
}
rng = np.random.RandomState([2017, 8, 30])
# check if we've trained before, and if we have, use that pre-trained model
if os.path.exists(model_path + ".meta"):
tf_model_load(sess, model_path)
else:
train(sess, loss, x_train, y_train, args=train_params, rng=rng)
saver = tf.train.Saver()
saver.save(sess, model_path)
# Evaluate the accuracy of the MNIST model on legitimate test examples
eval_params = {"batch_size": batch_size}
accuracy = model_eval(sess, x, y, preds, x_test, y_test, args=eval_params)
assert x_test.shape[0] == test_end - test_start, x_test.shape
print("Test accuracy on legitimate test examples: {0}".format(accuracy))
report.clean_train_clean_eval = accuracy
###########################################################################
# Craft adversarial examples using Carlini and Wagner's approach
###########################################################################
nb_adv_per_sample = str(nb_classes - 1) if targeted else "1"
print(
"Crafting "
+ str(source_samples)
+ " * "
+ nb_adv_per_sample
+ " adversarial examples"
)
print("This could take some time ...")
# Instantiate a CW attack object
cw = CarliniWagnerL2(model, sess=sess)
if viz_enabled:
assert source_samples == nb_classes
idxs = [
np.where(np.argmax(y_test, axis=1) == i)[0][0] for i in range(nb_classes)
]
if targeted:
if viz_enabled:
# Initialize our array for grid visualization
grid_shape = (nb_classes, nb_classes, img_rows, img_cols, nchannels)
grid_viz_data = np.zeros(grid_shape, dtype="f")
adv_inputs = np.array(
[[instance] * nb_classes for instance in x_test[idxs]], dtype=np.float32
)
else:
adv_inputs = np.array(
[[instance] * nb_classes for instance in x_test[:source_samples]],
dtype=np.float32,
)
one_hot = np.zeros((nb_classes, nb_classes))
one_hot[np.arange(nb_classes), np.arange(nb_classes)] = 1
adv_inputs = adv_inputs.reshape(
(source_samples * nb_classes, img_rows, img_cols, nchannels)
)
adv_ys = np.array([one_hot] * source_samples, dtype=np.float32).reshape(
(source_samples * nb_classes, nb_classes)
)
yname = "y_target"
else:
if viz_enabled:
# Initialize our array for grid visualization
grid_shape = (nb_classes, 2, img_rows, img_cols, nchannels)
grid_viz_data = np.zeros(grid_shape, dtype="f")
adv_inputs = x_test[idxs]
else:
adv_inputs = x_test[:source_samples]
adv_ys = None
yname = "y"
if targeted:
cw_params_batch_size = source_samples * nb_classes
else:
cw_params_batch_size = source_samples
cw_params = {
"binary_search_steps": 1,
yname: adv_ys,
"max_iterations": attack_iterations,
"learning_rate": CW_LEARNING_RATE,
"batch_size": cw_params_batch_size,
"initial_const": 10,
}
adv = cw.generate_np(adv_inputs, **cw_params)
eval_params = {"batch_size": np.minimum(nb_classes, source_samples)}
if targeted:
adv_accuracy = model_eval(sess, x, y, preds, adv, adv_ys, args=eval_params)
else:
if viz_enabled:
err = model_eval(sess, x, y, preds, adv, y_test[idxs], args=eval_params)
adv_accuracy = 1 - err
else:
err = model_eval(
sess, x, y, preds, adv, y_test[:source_samples], args=eval_params
)
adv_accuracy = 1 - err
if viz_enabled:
for j in range(nb_classes):
if targeted:
for i in range(nb_classes):
grid_viz_data[i, j] = adv[i * nb_classes + j]
else:
grid_viz_data[j, 0] = adv_inputs[j]
grid_viz_data[j, 1] = adv[j]
print(grid_viz_data.shape)
print("--------------------------------------")
# Compute the number of adversarial examples that were successfully found
print("Avg. rate of successful adv. examples {0:.4f}".format(adv_accuracy))
report.clean_train_adv_eval = 1.0 - adv_accuracy
# Compute the average distortion introduced by the algorithm
percent_perturbed = np.mean(np.sum((adv - adv_inputs) ** 2, axis=(1, 2, 3)) ** 0.5)
print("Avg. L_2 norm of perturbations {0:.4f}".format(percent_perturbed))
# Close TF session
sess.close()
# Finally, block & display a grid of all the adversarial examples
if viz_enabled:
_ = grid_visual(grid_viz_data)
return report
def mnist_tutorial_jsma(train_start=0,
train_end=60000,
test_start=0,
test_end=10000,
viz_enabled=True,
nb_epochs=6,
batch_size=128,
nb_classes=10,
source_samples=10,
learning_rate=0.001):
"""
MNIST tutorial for the Jacobian-based saliency map approach (JSMA)
:param train_start: index of first training set example
:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
:param viz_enabled: (boolean) activate plots of adversarial examples
:param nb_epochs: number of epochs to train model
:param batch_size: size of training batches
:param nb_classes: number of output classes
:param source_samples: number of test inputs to attack
:param learning_rate: learning rate for training
:return: an AccuracyReport object
"""
# Object used to keep track of (and return) key accuracies
report = AccuracyReport()
# MNIST-specific dimensions
img_rows = 28
img_cols = 28
channels = 1
# Set TF random seed to improve reproducibility
tf.set_random_seed(4254264)
set_log_level(logging.DEBUG)
# Get MNIST test data
# X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start,
# train_end=train_end,
# test_start=test_start,
# test_end=test_end)
# Get notMNIST data
# with np.load("notmnist.npz") as data:
# X_train, Y_train, X_test, Y_test = data['examples_train'], data['labels_train'], data['examples_test'], data['labels_test']
# Get MNISTnotMNIST data
with np.load("mnist.npz") as data:
X_train, Y_train, X_test, Y_test = data['X_train'], data[
'Y_train'], data['X_test'], data['Y_test']
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
y = tf.placeholder(tf.float32, shape=(None, 10))
# Create TF session and set as Keras backend session
sess = tf.Session()
print("Created TensorFlow session.")
# Define TF model graph
model_path = "./"
model_name = "clean_trained_mnist_model"
model = make_basic_cnn(nb_classes=nb_classes)
if tf_model_load(sess, file_path=os.path.join(model_path, model_name)):
print(model_name, " reloaded.")
preds = model.get_probs(x)
# print('shape is', preds.get_shape())
# clean_train = True
# if clean_train:
# train_params = {
# 'nb_epochs': nb_epochs,
# 'batch_size': batch_size,
# 'learning_rate': learning_rate
# }
# model_path = "./"
# model_name = "clean_trained__model_notmnist"
# rng = np.random.RandomState([1989, 12, 13])
# model = make_basic_cnn()
# preds = model.get_probs(x)
#
# def evaluate():
# # Evaluate the accuracy of the MNIST model on legitimate test
# # examples
# eval_params = {'batch_size': batch_size}
# acc = model_eval(
# sess, x, y, preds, X_test, Y_test, args=eval_params)
# report.clean_train_clean_eval = acc
# assert X_test.shape[0] == test_end - test_start, X_test.shape
# print('Test accuracy on legitimate examples: %0.4f' % acc)
# model_train(sess, x, y, preds, X_train, Y_train, evaluate=evaluate,args=train_params, rng=rng)
#
# save_path = os.path.join(model_path, model_name)
# saver = tf.train.Saver()
# saver.save(sess, save_path)
# _logger.info("Completed model training and saved at: " + str(save_path))
# print("Defined TensorFlow model graph.")
###########################################################################
# Training the model using TensorFlow
###########################################################################
# Train an MNIST model
# train_params = {
# 'nb_epochs': nb_epochs,
# 'batch_size': batch_size,
# 'learning_rate': learning_rate,
# 'train_dir': model_path,
# 'filename': model_name
# }
# sess.run(tf.global_variables_initializer())
# rng = np.random.RandomState([2017, 8, 30])
# model_train(sess, x, y, preds, X_train, Y_train, save=True, args=train_params,
# rng=rng)
# Evaluate the accuracy of the MNIST model on legitimate test examples
eval_params = {'batch_size': batch_size}
accuracy = model_eval(sess, x, y, preds, X_test, Y_test, args=eval_params)
assert X_test.shape[0] == test_end - test_start, X_test.shape
print('Test accuracy on legitimate test examples: {0}'.format(accuracy))
# report.clean_train_clean_eval = accuracy
###########################################################################
# Craft adversarial examples using the Jacobian-based saliency map approach
###########################################################################
print('Crafting ' + str(source_samples) + ' * ' + str(nb_classes - 1) +
' adversarial examples')
# Keep track of success (adversarial example classified in target)
results = np.zeros((nb_classes, source_samples), dtype='i')
# misclassify
results2 = np.zeros((nb_classes, source_samples), dtype='i')
# Rate of perturbed features for each test set example and target class
perturbations = np.zeros((nb_classes, source_samples), dtype='f')
# Initialize our array for grid visualization
# grid_shape = (nb_classes, nb_classes, img_rows, img_cols, channels)
# grid_viz_data = np.zeros(grid_shape, dtype='f')
# Instantiate a SaliencyMapMethod attack object
jsma = SaliencyMapMethod(model, back='tf', sess=sess)
jsma_params = {
'theta': 1,
'gamma': 0.1,
'clip_min': 0.,
'clip_max': 1.,
'y_target': None
}
figure = None
rng = np.random.RandomState([1358, 23, 234])
index_shuf = list(range(len(X_test)))
rng.shuffle(index_shuf)
X_test = X_test[index_shuf]
Y_test = Y_test[index_shuf]
# create a dictionary to keep track of occurence of each letter
# create a 2D array to kee track of successful attacks
occurence = {0: 0, 1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0}
# 10:0, 11:0, 12:0, 13:0, 14:0, 15:0, 16:0, 17:0, 18:0, 19:0}
rate_table = np.zeros((nb_classes, nb_classes), dtype='f')
# Loop over the samples we want to perturb into adversarial examples
for sample_ind in xrange(0, source_samples):
print('--------------------------------------')
print('Attacking input %i/%i' % (sample_ind + 1, source_samples))
sample = X_test[sample_ind:(sample_ind + 1)]
# We want to find an adversarial example for each possible target class
# (i.e. all classes that differ from the label given in the dataset)
current_class = int(np.argmax(Y_test[sample_ind]))
target_classes = other_classes(nb_classes, current_class)
# add one to current class occurence
occurence[current_class] += 1
# For the grid visualization, keep original images along the diagonal
# grid_viz_data[current_class, current_class, :, :, :] = np.reshape(
# sample, (img_rows, img_cols, channels))
# Loop over all target classes
for target in target_classes:
print('Generating adv. example for target class %i' % target)
# This call runs the Jacobian-based saliency map approach
one_hot_target = np.zeros((1, nb_classes), dtype=np.float32)
one_hot_target[0, target] = 1
jsma_params['y_target'] = one_hot_target
adv_x = jsma.generate_np(sample, **jsma_params)
# Check if success was achieved
res = int(model_argmax(sess, x, preds, adv_x) == target)
# misclassify
res2 = int(model_argmax(sess, x, preds, adv_x) != current_class)
# if success, add one to successful rate table
if res == 1:
rate_table[current_class, target] += 1.
# Computer number of modified features
adv_x_reshape = adv_x.reshape(-1)
test_in_reshape = X_test[sample_ind].reshape(-1)
nb_changed = np.where(adv_x_reshape != test_in_reshape)[0].shape[0]
percent_perturb = float(nb_changed) / adv_x.reshape(-1).shape[0]
# Display the original and adversarial images side-by-side
# if viz_enabled:
# figure = pair_visual(
# np.reshape(sample, (img_rows, img_cols)),
# np.reshape(adv_x, (img_rows, img_cols)), figure)
# Add our adversarial example to our grid data
# grid_viz_data[target, current_class, :, :, :] = np.reshape(
# adv_x, (img_rows, img_cols, channels))
# Update the arrays for later analysis
results[target, sample_ind] = res
results2[target, sample_ind] = res2
perturbations[target, sample_ind] = percent_perturb
print('--------------------------------------')
# Close TF session
sess.close()
# Compute success rate of each letter attacking each target
for cur in range(nb_classes):
if occurence[cur] != 0:
rate_table[cur, :] /= float(occurence[cur])
print("The table of rate of successful attacking is shown below")
print(rate_table)
print("the number of occurrence of each class is ", occurence)
# Compute the number of adversarial examples that were successfully found
nb_targets_tried = ((nb_classes - 1) * source_samples)
succ_rate = float(np.sum(results)) / nb_targets_tried
# misclassify
succ_rate2 = float(np.sum(results2)) / nb_targets_tried
print('Avg. rate of successful adv. examples {0:.4f}'.format(succ_rate))
print(
'Avg. rate of misclassified adv. examples {0:.4f}'.format(succ_rate2))
# report.clean_train_adv_eval = 1. - succ_rate
# Compute the average distortion introduced by the algorithm
percent_perturbed = np.mean(perturbations)
print('Avg. rate of perturbed features {0:.4f}'.format(percent_perturbed))
# Compute the average distortion introduced for successful samples only
percent_perturb_succ = np.mean(perturbations * (results == 1))
print('Avg. rate of perturbed features for successful '
'adversarial examples {0:.4f}'.format(percent_perturb_succ))
# Finally, block & display a grid of all the adversarial examples
# if viz_enabled:
# import matplotlib.pyplot as plt
# plt.close(figure)
# _ = grid_visual(grid_viz_data)
return report
def cifar10_tutorial(train_start=0,
train_end=60000,
test_start=0,
test_end=10000,
nb_epochs=NB_EPOCHS,
batch_size=BATCH_SIZE,
architecture=ARCHITECTURE,
load_model=LOAD_MODEL,
ckpt_dir='None',
learning_rate=LEARNING_RATE,
clean_train=CLEAN_TRAIN,
backprop_through_attack=BACKPROP_THROUGH_ATTACK,
nb_filters=NB_FILTERS,
num_threads=None,
label_smoothing=0.):
"""
CIFAR10 cleverhans tutorial
:param train_start: index of first training set example
:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
:param nb_epochs: number of epochs to train model
:param batch_size: size of training batches
:param learning_rate: learning rate for training
:param clean_train: perform normal training on clean examples only
before performing adversarial training.
:param backprop_through_attack: If True, backprop through adversarial
example construction process during
adversarial training.
:param label_smoothing: float, amount of label smoothing for cross entropy
:return: an AccuracyReport object
"""
# Object used to keep track of (and return) key accuracies
report = AccuracyReport()
# Set TF random seed to improve reproducibility
tf.set_random_seed(int(time.time() * 1000) % 2**31)
np.random.seed(int(time.time() * 1001) % 2**31)
# Set logging level to see debug information
set_log_level(logging.DEBUG)
# Create TF session
if num_threads:
config_args = dict(intra_op_parallelism_threads=1)
else:
config_args = {}
sess = tf.Session(config=tf.ConfigProto(**config_args))
# Get CIFAR10 data
data = CIFAR10(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end)
dataset_size = data.x_train.shape[0]
dataset_train = data.to_tensorflow()[0]
dataset_train = dataset_train.map(
lambda x, y: (random_shift(random_horizontal_flip(x)), y), 4)
dataset_train = dataset_train.batch(batch_size)
dataset_train = dataset_train.prefetch(16)
x_train, y_train = data.get_set('train')
pgd_train = None
if FLAGS.load_pgd_train_samples:
pgd_path = os.path.expanduser('~/data/advhyp/{}/samples'.format(
FLAGS.load_pgd_train_samples))
x_train = np.load(os.path.join(pgd_path, 'train_clean.npy'))
y_train = np.load(os.path.join(pgd_path, 'train_y.npy'))
pgd_train = np.load(os.path.join(pgd_path, 'train_pgd.npy'))
if x_train.shape[1] == 3:
x_train = x_train.transpose((0, 2, 3, 1))
pgd_train = pgd_train.transpose((0, 2, 3, 1))
if len(y_train.shape) == 1:
y_tmp = np.zeros((len(y_train), np.max(y_train) + 1),
y_train.dtype)
y_tmp[np.arange(len(y_tmp)), y_train] = 1.
y_train = y_tmp
x_test, y_test = data.get_set('test')
pgd_test = None
if FLAGS.load_pgd_test_samples:
pgd_path = os.path.expanduser('~/data/advhyp/{}/samples'.format(
FLAGS.load_pgd_test_samples))
x_test = np.load(os.path.join(pgd_path, 'test_clean.npy'))
y_test = np.load(os.path.join(pgd_path, 'test_y.npy'))
pgd_test = np.load(os.path.join(pgd_path, 'test_pgd.npy'))
if x_test.shape[1] == 3:
x_test = x_test.transpose((0, 2, 3, 1))
pgd_test = pgd_test.transpose((0, 2, 3, 1))
if len(y_test.shape) == 1:
y_tmp = np.zeros((len(y_test), np.max(y_test) + 1), y_test.dtype)
y_tmp[np.arange(len(y_tmp)), y_test] = 1.
y_test = y_tmp
train_idcs = np.arange(len(x_train))
np.random.shuffle(train_idcs)
x_train, y_train = x_train[train_idcs], y_train[train_idcs]
if pgd_train is not None:
pgd_train = pgd_train[train_idcs]
test_idcs = np.arange(len(x_test))[:FLAGS.test_size]
np.random.shuffle(test_idcs)
x_test, y_test = x_test[test_idcs], y_test[test_idcs]
if pgd_test is not None:
pgd_test = pgd_test[test_idcs]
# Use Image Parameters
img_rows, img_cols, nchannels = x_test.shape[1:4]
nb_classes = y_test.shape[1]
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, nchannels))
y = tf.placeholder(tf.float32, shape=(None, nb_classes))
# Train an MNIST model
train_params = {
'nb_epochs': nb_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate
}
eval_params = {'batch_size': batch_size}
pgd_params = {
# ord: ,
'eps': FLAGS.eps,
'eps_iter': (FLAGS.eps / 5),
'nb_iter': 10,
'clip_min': 0,
'clip_max': 255
}
cw_params = {
'binary_search_steps': FLAGS.cw_search_steps,
'max_iterations': FLAGS.cw_steps, #1000
'abort_early': True,
'learning_rate': FLAGS.cw_lr,
'batch_size': batch_size,
'confidence': 0,
'initial_const': FLAGS.cw_c,
'clip_min': 0,
'clip_max': 255
}
# Madry dosen't divide by 255
x_train *= 255
x_test *= 255
if pgd_train is not None:
pgd_train *= 255
if pgd_test is not None:
pgd_test *= 255
print('x_train amin={} amax={}'.format(np.amin(x_train), np.amax(x_train)))
print('x_test amin={} amax={}'.format(np.amin(x_test), np.amax(x_test)))
print(
'clip_min : {}, clip_max : {} >> CHECK WITH WHICH VALUES THE CLASSIFIER WAS PRETRAINED !!! <<'
.format(pgd_params['clip_min'], pgd_params['clip_max']))
rng = np.random.RandomState() # [2017, 8, 30]
debug_dict = dict() if FLAGS.save_debug_dict else None
def do_eval(preds,
x_set,
y_set,
report_key,
is_adv=None,
predictor=None,
x_adv=None):
if predictor is None:
acc = model_eval(sess, x, y, preds, x_set, y_set, args=eval_params)
else:
do_eval(preds, x_set, y_set, report_key, is_adv=is_adv)
if x_adv is not None:
x_set_adv, = batch_eval(sess, [x], [x_adv], [x_set],
batch_size=batch_size)
assert x_set.shape == x_set_adv.shape
x_set = x_set_adv
n_batches = math.ceil(x_set.shape[0] / batch_size)
p_set, p_det = np.concatenate([
predictor.send(x_set[b * batch_size:(b + 1) * batch_size])
for b in tqdm.trange(n_batches)
]).T
acc = np.equal(p_set, y_set[:len(p_set)].argmax(-1)).mean()
# if is_adv:
# import IPython ; IPython.embed() ; exit(1)
if FLAGS.save_debug_dict:
debug_dict['x_set'] = x_set
debug_dict['y_set'] = y_set
ddfn = 'logs/debug_dict_{}.pkl'.format(
'adv' if is_adv else 'clean')
if not os.path.exists(ddfn):
with open(ddfn, 'wb') as f:
pickle.dump(debug_dict, f)
debug_dict.clear()
if is_adv is None:
report_text = None
elif is_adv:
report_text = 'adversarial'
else:
report_text = 'legitimate'
if report_text:
print('Test accuracy on %s examples %s: %0.4f' %
(report_text, 'with correction'
if predictor is not None else 'without correction', acc))
if is_adv is not None:
label = 'test_acc_{}_{}'.format(
report_text, 'corrected' if predictor else 'uncorrected')
swriter.add_scalar(label, acc)
if predictor is not None:
detect = np.equal(p_det, is_adv).mean()
label = 'test_det_{}_{}'.format(
report_text,
'corrected' if predictor else 'uncorrected')
print(label, detect)
swriter.add_scalar(label, detect)
label = 'test_dac_{}_{}'.format(
report_text,
'corrected' if predictor else 'uncorrected')
swriter.add_scalar(
label,
np.equal(p_set,
y_set[:len(p_set)].argmax(-1))[np.equal(
p_det, is_adv)].mean())
return acc
if clean_train:
if architecture == 'ConvNet':
model = ModelAllConvolutional('model1',
nb_classes,
nb_filters,
input_shape=[32, 32, 3])
elif architecture == 'ResNet':
model = ResNet(scope='ResNet')
else:
raise Exception('Specify valid classifier architecture!')
preds = model.get_logits(x)
loss = CrossEntropy(model, smoothing=label_smoothing)
if load_model:
model_name = 'naturally_trained'
if FLAGS.load_adv_trained:
model_name = 'adv_trained'
if ckpt_dir is not 'None':
ckpt = tf.train.get_checkpoint_state(
os.path.join(os.path.expanduser(ckpt_dir), model_name))
else:
ckpt = tf.train.get_checkpoint_state('./models/' + model_name)
ckpt_path = False if ckpt is None else ckpt.model_checkpoint_path
saver = tf.train.Saver(var_list=dict(
(v.name.split('/', 1)[1].split(':')[0], v)
for v in tf.global_variables()))
saver.restore(sess, ckpt_path)
print('\nMODEL SUCCESSFULLY LOADED from : {}'.format(ckpt_path))
initialize_uninitialized_global_variables(sess)
else:
def evaluate():
do_eval(preds, x_test, y_test, 'clean_train_clean_eval', False)
train(sess,
loss,
None,
None,
dataset_train=dataset_train,
dataset_size=dataset_size,
evaluate=evaluate,
args=train_params,
rng=rng,
var_list=model.get_params())
logits_op = preds.op
while logits_op.type != 'MatMul':
logits_op = logits_op.inputs[0].op
latent_x_tensor, weights = logits_op.inputs
logits_tensor = preds
nb_classes = weights.shape[-1].value
if not FLAGS.save_pgd_samples:
noise_eps = FLAGS.noise_eps.split(',')
if FLAGS.noise_eps_detect is None:
FLAGS.noise_eps_detect = FLAGS.noise_eps
noise_eps_detect = FLAGS.noise_eps_detect.split(',')
if pgd_train is not None:
pgd_train = pgd_train[:FLAGS.n_collect]
if not FLAGS.passthrough:
predictor = tf_robustify.collect_statistics(
x_train[:FLAGS.n_collect],
y_train[:FLAGS.n_collect],
x,
sess,
logits_tensor=logits_tensor,
latent_x_tensor=latent_x_tensor,
weights=weights,
nb_classes=nb_classes,
p_ratio_cutoff=FLAGS.p_ratio_cutoff,
noise_eps=noise_eps,
noise_eps_detect=noise_eps_detect,
pgd_eps=pgd_params['eps'],
pgd_lr=pgd_params['eps_iter'] / pgd_params['eps'],
pgd_iters=pgd_params['nb_iter'],
save_alignments_dir='logs/stats'
if FLAGS.save_alignments else None,
load_alignments_dir=os.path.expanduser(
'~/data/advhyp/madry/stats')
if FLAGS.load_alignments else None,
clip_min=pgd_params['clip_min'],
clip_max=pgd_params['clip_max'],
batch_size=batch_size,
num_noise_samples=FLAGS.num_noise_samples,
debug_dict=debug_dict,
debug=FLAGS.debug,
targeted=False,
pgd_train=pgd_train,
fit_classifier=FLAGS.fit_classifier,
clip_alignments=FLAGS.clip_alignments,
just_detect=FLAGS.just_detect)
else:
def _predictor():
_x = yield
while (_x is not None):
_y = sess.run(preds, {x: _x}).argmax(-1)
_x = yield np.stack((_y, np.zeros_like(_y)), -1)
predictor = _predictor()
next(predictor)
if FLAGS.save_alignments:
exit(0)
# Evaluate the accuracy of the model on clean examples
acc_clean = do_eval(preds,
x_test,
y_test,
'clean_train_clean_eval',
False,
predictor=predictor)
# Initialize the PGD attack object and graph
if FLAGS.attack == 'pgd':
pgd = MadryEtAl(model, sess=sess)
adv_x = pgd.generate(x, **pgd_params)
elif FLAGS.attack == 'cw':
cw = CarliniWagnerL2(model, sess=sess)
adv_x = cw.generate(x, **cw_params)
elif FLAGS.attack == 'mean':
pgd = MadryEtAl(model, sess=sess)
mean_eps = FLAGS.mean_eps * FLAGS.eps
def _attack_mean(x):
x_many = tf.tile(x[None], (FLAGS.mean_samples, 1, 1, 1))
x_noisy = x_many + tf.random_uniform(x_many.shape, -mean_eps,
mean_eps)
x_noisy = tf.clip_by_value(x_noisy, 0, 255)
x_pgd = pgd.generate(x_noisy, **pgd_params)
x_clip = tf.minimum(x_pgd, x_many + FLAGS.eps)
x_clip = tf.maximum(x_clip, x_many - FLAGS.eps)
x_clip = tf.clip_by_value(x_clip, 0, 255)
return x_clip
adv_x = tf.map_fn(_attack_mean, x)
adv_x = tf.reduce_mean(adv_x, 1)
preds_adv = model.get_logits(adv_x)
if FLAGS.save_pgd_samples:
for ds, y, name in ((x_train, y_train, 'train'), (x_test, y_test,
'test')):
train_batches = math.ceil(len(ds) / FLAGS.batch_size)
train_pgd = np.concatenate([
sess.run(adv_x, {
x:
ds[b * FLAGS.batch_size:(b + 1) * FLAGS.batch_size]
}) for b in tqdm.trange(train_batches)
])
np.save('logs/{}_clean.npy'.format(name), ds / 255.)
np.save('logs/{}_y.npy'.format(name), y)
train_pgd /= 255.
np.save('logs/{}_pgd.npy'.format(name), train_pgd)
exit(0)
# Evaluate the accuracy of the model on adversarial examples
if not FLAGS.load_pgd_test_samples:
acc_pgd = do_eval(preds_adv,
x_test,
y_test,
'clean_train_adv_eval',
True,
predictor=predictor,
x_adv=adv_x)
else:
acc_pgd = do_eval(preds,
pgd_test,
y_test,
'clean_train_adv_eval',
True,
predictor=predictor)
swriter.add_scalar('test_acc_mean', (acc_clean + acc_pgd) / 2., 0)
print('Repeating the process, using adversarial training')
exit(0)
# Create a new model and train it to be robust to MadryEtAl
if architecture == 'ConvNet':
model2 = ModelAllConvolutional('model2',
nb_classes,
nb_filters,
input_shape=[32, 32, 3])
elif architecture == 'ResNet':
model = ResNet()
else:
raise Exception('Specify valid classifier architecture!')
pgd2 = MadryEtAl(model2, sess=sess)
def attack(x):
return pgd2.generate(x, **pgd_params)
loss2 = CrossEntropy(model2, smoothing=label_smoothing, attack=attack)
preds2 = model2.get_logits(x)
adv_x2 = attack(x)
if not backprop_through_attack:
# For some attacks, enabling this flag increases the cost of
# training, but gives the defender the ability to anticipate how
# the atacker will change their strategy in response to updates to
# the defender's parameters.
adv_x2 = tf.stop_gradient(adv_x2)
preds2_adv = model2.get_logits(adv_x2)
if load_model:
if ckpt_dir is not 'None':
ckpt = tf.train.get_checkpoint_state(
os.path.join(os.path.expanduser(ckpt_dir), 'adv_trained'))
else:
ckpt = tf.train.get_checkpoint_state('./models/adv_trained')
ckpt_path = False if ckpt is None else ckpt.model_checkpoint_path
assert ckpt_path and tf_model_load(
sess, file_path=ckpt_path), '\nMODEL LOADING FAILED'
print('\nMODEL SUCCESSFULLY LOADED from : {}'.format(ckpt_path))
initialize_uninitialized_global_variables(sess)
else:
def evaluate2():
# Accuracy of adversarially trained model on legitimate test inputs
do_eval(preds2, x_test, y_test, 'adv_train_clean_eval', False)
# Accuracy of the adversarially trained model on adversarial
# examples
do_eval(preds2_adv, x_test, y_test, 'adv_train_adv_eval', True)
# Perform and evaluate adversarial training
train(sess,
loss2,
None,
None,
dataset_train=dataset_train,
dataset_size=dataset_size,
evaluate=evaluate2,
args=train_params,
rng=rng,
var_list=model2.get_params())
# Evaluate model
do_eval(preds2, x_test, y_test, 'adv_train_clean_eval', False)
do_eval(preds2_adv, x_test, y_test, 'adv_train_adv_eval', True)
return report
def mnist_tutorial_cw(train_start=0,
train_end=60000,
test_start=0,
test_end=10000,
viz_enabled=False,
nb_epochs=6,
batch_size=128,
nb_classes=10,
source_samples=1,
learning_rate=0.001,
attack_iterations=100,
model_path=os.path.join("models", "mnist"),
targeted=True):
"""
MNIST tutorial for Carlini and Wagner's attack
:param train_start: index of first training set example
:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
:param viz_enabled: (boolean) activate plots of adversarial examples
:param nb_epochs: number of epochs to train model
:param batch_size: size of training batches
:param nb_classes: number of output classes
:param source_samples: number of test inputs to attack
:param learning_rate: learning rate for training
:param model_path: path to the model file
:param targeted: should we run a targeted attack? or untargeted?
:return: an AccuracyReport object
"""
# Object used to keep track of (and return) key accuracies
report = AccuracyReport()
file = read_mat_file(filename)
label = file["label"]
data = file["data"]
#data[data>1]= 1
#data[data<0]= 0
adv_data = data[10000:80000, :, :, :]
cw = adv_data[0::7, :, :, :]
fgsm01 = adv_data[1::7, :, :, :]
fgsm03 = adv_data[2::7, :, :, :]
fgsm05 = adv_data[3::7, :, :, :]
gaussian01 = adv_data[4::7, :, :, :]
gaussian03 = adv_data[5::7, :, :, :]
gaussian05 = adv_data[6::7, :, :, :]
# MNIST-specific dimensions
img_rows = 28
img_cols = 28
channels = 1
# Set TF random seed to improve reproducibility
#tf.set_random_seed(1234)
# Create TF session
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
#config.log_device_placement=True
sess = tf.Session(config=config)
print("Created TensorFlow session.")
set_log_level(logging.DEBUG)
# Get MNIST test data
X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end)
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, channels))
y = tf.placeholder(tf.float32, shape=(None, nb_classes))
# Define TF model graph
model = make_basic_cnn()
preds = model(x)
print("Defined TensorFlow model graph.")
###########################################################################
# Training the model using TensorFlow
###########################################################################
train_params = {
'nb_epochs': nb_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate,
'train_dir': os.path.join(*os.path.split(model_path)[:-1]),
'filename': os.path.split(model_path)[-1]
}
rng = np.random.RandomState([2017, 8, 30])
# check if we've trained before, and if we have, use that pre-trained model
if os.path.exists(model_path + ".meta"):
tf_model_load(sess, model_path)
else:
model_train(sess,
x,
y,
preds,
X_train,
Y_train,
args=train_params,
save=os.path.exists("models"),
rng=rng)
# Evaluate the accuracy of the MNIST model on legitimate test examples
eval_params = {'batch_size': batch_size}
pdb.set_trace()
accuracy = model_eval(sess, x, y, preds, X_test, Y_test, args=eval_params)
assert X_test.shape[0] == test_end - test_start, X_test.shape
print('Test accuracy on legitimate test examples: {0}'.format(accuracy))
report.clean_train_clean_eval = accuracy
def mnist_tutorial_deepfool(train_start=0, train_end=60000, #读60000训练
test_start=0,test_end=10000, #读10000测试
viz_enabled=True, nb_epochs=6,
batch_size=128, nb_classes=2, source_samples=10,
learning_rate=0.001, attack_iterations=100,
model_path=os.path.join("models", "mnist")):
"""
MNIST tutorial for Deepfool's attack
:param train_start: index of first training set example
:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
:param viz_enabled: (boolean) activate plots of adversarial examples激活对抗例子
:param nb_epochs: number of epochs to train model（一个epoch指代所有的数据送入网络中完成一次前向计算及反向传播的过程。）
:param batch_size: size of training batches
:param nb_classes: number of output classes（输出几类）
:param source_samples: number of test inputs to attack（测试输入用于攻击的数量）
:param learning_rate: learning rate for training（学习率）
:param model_path: path to the model file（文件路径）
:param attack_iterations: 攻击迭代次数
:return: an AccuracyReport object
"""
# Object used to keep track of (and return) key accuracies精确度报告
report = AccuracyReport()
# MNIST-specific dimensions图像尺寸28*28*1
img_rows = 28
img_cols = 28
channels = 1
# Set TF random seed to improve reproducibility
tf.set_random_seed(1234)
# Create TF session
sess = tf.Session()
print("Created TensorFlow session.")
set_log_level(logging.DEBUG)
# Get MNIST test data
X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end)
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
y = tf.placeholder(tf.float32, shape=(None, 10))
# Define TF model graph
model = make_basic_picklable_cnn()
preds = model(x)
print("Defined TensorFlow model graph.")
###########################################################################
# Training the model using TensorFlow(构建训练模型)
###########################################################################
# Train an MNIST model
train_params = {
'nb_epochs': nb_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate,
'train_dir': os.path.join(*os.path.split(model_path)[:-1]),
'filename': os.path.split(model_path)[-1]
}
rng = np.random.RandomState([2018, 8, 9])
# check if we've trained before, and if we have, use that pre-trained model
if os.path.exists(model_path+".meta"):
tf_model_load(sess, model_path)
else:
model_train(sess, x, y, preds, X_train, Y_train, args=train_params,
save=os.path.exists("models"), rng=rng)
print("save success")
# Evaluate the accuracy of the MNIST model on legitimate test examples
eval_params = {'batch_size': batch_size}
accuracy = model_eval(sess, x, y, preds, X_test, Y_test, args=eval_params)
assert X_test.shape[0] == test_end - test_start, X_test.shape
print('Test accuracy on legitimate test examples: {0}'.format(accuracy))
report.clean_train_clean_eval = accuracy
###########################################################################
# Craft adversarial examples using Carlini and Wagner's approach
###########################################################################
print('Crafting ' + str(source_samples) + ' * ' + str(nb_classes-1) +
' adversarial examples')
print("This could take some time ...")
# Instantiate a DeepFool attack object
deepfool = DeepFool(model, back='tf', sess=sess)
idxs = [np.where(np.argmax(Y_test, axis=1) == i)[0][1] for i in range(10)]
print("idxs:",idxs)
# construct adv_inputs
grid_shape = (nb_classes, 2, img_rows, img_cols, channels)
grid_viz_data = np.zeros(grid_shape, dtype='f')
print("grid_viz_data",grid_viz_data.shape)
adv_inputs = X_test[idxs].reshape([-1,28,28,1])
deepfool_params = {'nb_candidate': 10,
'overshoot': 0.02,
'max_iter': attack_iterations,
'nb_classes': 10,
'clip_min': 0.,
'clip_max': 1.}
adv = deepfool.generate_np(adv_inputs, **deepfool_params)
print("adv success")
adv_accuracy = 1-model_eval(sess, x, y, preds, adv, Y_test[idxs],
args={'batch_size': 10})
for j in range(10):
grid_viz_data[j, 0] = adv_inputs[j]
grid_viz_data[j, 1] = adv[j]
print(grid_viz_data.shape)
print('--------------------------------------')
# Compute the number of adversarial examples that were successfully found
print('Avg. rate of successful adv. examples {0:.4f}'.format(adv_accuracy))
report.clean_train_adv_eval = 1.-adv_accuracy
# Compute the average distortion introduced by the algorithm
percent_perturbed = np.mean(np.sum((adv - adv_inputs)**2,
axis=(1, 2, 3))**.5)
print('Avg. L_2 norm of perturbations {0:.4f}'.format(percent_perturbed))
# Close TF session
sess.close()
# Finally, block & display a grid of all the adversarial examples
if viz_enabled:
import matplotlib.pyplot as plt
_ = grid_visual(grid_viz_data)
return report
def main(argv=None):
from cleverhans_tutorials import check_installation
check_installation(__file__)
if not os.path.exists( CONFIG.SAVE_PATH ):
os.makedirs( CONFIG.SAVE_PATH )
save_path_data = CONFIG.SAVE_PATH + 'data/'
if not os.path.exists( save_path_data ):
os.makedirs( save_path_data )
model_path = CONFIG.SAVE_PATH + '../all/' + CONFIG.DATASET + '/'
if not os.path.exists( model_path ):
os.makedirs( model_path )
os.makedirs( model_path + 'data/' )
nb_epochs = FLAGS.nb_epochs
batch_size = FLAGS.batch_size
learning_rate = FLAGS.learning_rate
nb_filters = FLAGS.nb_filters
len_x = int(CONFIG.NUM_TEST/2)
start = time.time()
# Object used to keep track of (and return) key accuracies
report = AccuracyReport()
# Set seeds to improve reproducibility
if CONFIG.DATASET == 'mnist' or CONFIG.DATASET == 'cifar10':
tf.set_random_seed(1234)
np.random.seed(1234)
rd.seed(1234)
elif CONFIG.DATASET == 'moon' or CONFIG.DATASET == 'dims':
tf.set_random_seed(13)
np.random.seed(1234)
rd.seed(0)
# Set logging level to see debug information
set_log_level(logging.DEBUG)
# Create TF session
tf_config = tf.ConfigProto(allow_soft_placement=True,log_device_placement=True)
tf_config.gpu_options.per_process_gpu_memory_fraction = 0.2
sess = tf.Session(config=tf_config)
if CONFIG.DATASET == 'mnist':
# Get MNIST data
mnist = MNIST(train_start=0, train_end=CONFIG.NUM_TRAIN,
test_start=0, test_end=CONFIG.NUM_TEST)
x_train, y_train = mnist.get_set('train')
x_test, y_test = mnist.get_set('test')
elif CONFIG.DATASET == 'cifar10':
# Get CIFAR10 data
data = CIFAR10(train_start=0, train_end=CONFIG.NUM_TRAIN,
test_start=0, test_end=CONFIG.NUM_TEST)
dataset_size = data.x_train.shape[0]
dataset_train = data.to_tensorflow()[0]
dataset_train = dataset_train.map(
lambda x, y: (random_shift(random_horizontal_flip(x)), y), 4)
dataset_train = dataset_train.batch(batch_size)
dataset_train = dataset_train.prefetch(16)
x_train, y_train = data.get_set('train')
x_test, y_test = data.get_set('test')
elif CONFIG.DATASET == 'moon':
# Create a two moon example
X, y = make_moons(n_samples=(CONFIG.NUM_TRAIN+CONFIG.NUM_TEST), noise=0.2,
random_state=0)
X = StandardScaler().fit_transform(X)
x_train1, x_test1, y_train1, y_test1 = train_test_split(X, y,
test_size=(CONFIG.NUM_TEST/(CONFIG.NUM_TRAIN
+CONFIG.NUM_TEST)), random_state=0)
x_train, y_train, x_test, y_test = normalize_reshape_inputs_2d(model_path, x_train1,
y_train1, x_test1,
y_test1)
elif CONFIG.DATASET == 'dims':
X, y = make_moons(n_samples=(CONFIG.NUM_TRAIN+CONFIG.NUM_TEST), noise=0.2,
random_state=0)
X = StandardScaler().fit_transform(X)
x_train1, x_test1, y_train1, y_test1 = train_test_split(X, y,
test_size=(CONFIG.NUM_TEST/(CONFIG.NUM_TRAIN
+CONFIG.NUM_TEST)), random_state=0)
x_train2, y_train, x_test2, y_test = normalize_reshape_inputs_2d(model_path, x_train1,
y_train1,x_test1,
y_test1)
x_train, x_test = add_noise_and_QR(x_train2, x_test2, CONFIG.NUM_DIMS)
np.save(os.path.join(save_path_data, 'x_test'), x_test)
np.save(os.path.join(save_path_data, 'y_test'), y_test)
# Use Image Parameters
img_rows, img_cols, nchannels = x_train.shape[1:4]
nb_classes = y_train.shape[1]
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols,
nchannels))
y = tf.placeholder(tf.float32, shape=(None, nb_classes))
# Train an model
train_params = {
'nb_epochs': nb_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate
}
eval_params = {'batch_size': 1}
rng = np.random.RandomState([2017, 8, 30])
with open(CONFIG.SAVE_PATH + 'acc_param.txt', 'a') as fi:
def do_eval(adv_x, preds, x_set, y_set, report_key):
acc, pred_np, adv_x_np = model_eval(sess, x, y, preds, adv_x, nb_classes, x_set,
y_set, args=eval_params)
setattr(report, report_key, acc)
if report_key:
print('Accuracy on %s examples: %0.4f' % (report_key, acc), file=fi)
return pred_np, adv_x_np
if CONFIG.DATASET == 'mnist':
trained_model_path = model_path + 'data/trained_model'
model = ModelBasicCNN('model1', nb_classes, nb_filters)
elif CONFIG.DATASET == 'cifar10':
trained_model_path = model_path + 'data/trained_model'
model = ModelAllConvolutional('model1', nb_classes, nb_filters,
input_shape=[32, 32, 3])
elif CONFIG.DATASET == 'moon':
trained_model_path = model_path + 'data/trained_model'
model = ModelMLP('model1', nb_classes)
elif CONFIG.DATASET == 'dims':
trained_model_path = save_path_data + 'trained_model'
model = ModelMLP_dyn('model1', nb_classes, CONFIG.NUM_DIMS)
preds = model.get_logits(x)
loss = CrossEntropy(model, smoothing=0.1)
def evaluate():
_, _ = do_eval(x, preds, x_test, y_test, 'test during train')
if os.path.isfile( trained_model_path + '.index' ):
tf_model_load(sess, trained_model_path)
else:
if CONFIG.DATASET == 'mnist':
train(sess, loss, x_train, y_train, evaluate=evaluate,
args=train_params, rng=rng, var_list=model.get_params())
elif CONFIG.DATASET == 'cifar10':
train(sess, loss, None, None,
dataset_train=dataset_train, dataset_size=dataset_size,
evaluate=evaluate, args=train_params, rng=rng,
var_list=model.get_params())
elif CONFIG.DATASET == 'moon':
train_2d(sess, loss, x, y, x_train, y_train, save=False, evaluate=evaluate,
args=train_params, rng=rng, var_list=model.get_params())
elif CONFIG.DATASET == 'dims':
train_2d(sess, loss, x, y, x_train, y_train, evaluate=evaluate,
args=train_params, rng=rng, var_list=model.get_params())
saver = tf.train.Saver()
saver.save(sess, trained_model_path)
# Evaluate the accuracy on test examples
if os.path.isfile( save_path_data + 'logits_zero_attacked.npy' ):
logits_0 = np.load(save_path_data + 'logits_zero_attacked.npy')
else:
_, _ = do_eval(x, preds, x_train, y_train, 'train')
logits_0, _ = do_eval(x, preds, x_test, y_test, 'test')
np.save(os.path.join(save_path_data, 'logits_zero_attacked'), logits_0)
if CONFIG.DATASET == 'moon':
num_grid_points = 5000
if os.path.isfile( model_path + 'data/images_mesh' + str(num_grid_points) + '.npy' ):
x_mesh = np.load(model_path + 'data/images_mesh' + str(num_grid_points) + '.npy')
logits_mesh = np.load(model_path + 'data/logits_mesh' + str(num_grid_points) + '.npy')
else:
xx, yy = np.meshgrid(np.linspace(0, 1, num_grid_points), np.linspace(0, 1, num_grid_points))
x_mesh1 = np.stack([np.ravel(xx), np.ravel(yy)]).T
y_mesh1 = np.ones((x_mesh1.shape[0]),dtype='int64')
x_mesh, y_mesh, _, _ = normalize_reshape_inputs_2d(model_path, x_mesh1, y_mesh1)
logits_mesh, _ = do_eval(x, preds, x_mesh, y_mesh, 'mesh')
x_mesh = np.squeeze(x_mesh)
np.save(os.path.join(model_path, 'data/images_mesh'+str(num_grid_points)), x_mesh)
np.save(os.path.join(model_path, 'data/logits_mesh'+str(num_grid_points)), logits_mesh)
points_x = x_test[:len_x]
points_y = y_test[:len_x]
points_x_bar = x_test[len_x:]
points_y_bar = y_test[len_x:]
# Initialize the CW attack object and graph
cw = CarliniWagnerL2(model, sess=sess)
# first attack
attack_params = {
'learning_rate': CONFIG.CW_LEARNING_RATE,
'max_iterations': CONFIG.CW_MAX_ITERATIONS
}
if CONFIG.DATASET == 'moon':
out_a = compute_polytopes_a(x_mesh, logits_mesh, model_path)
attack_params['const_a_min'] = out_a
attack_params['const_a_max'] = 100
adv_x = cw.generate(x, **attack_params)
if os.path.isfile( save_path_data + 'images_once_attacked.npy' ):
adv_img_1 = np.load(save_path_data + 'images_once_attacked.npy')
logits_1 = np.load(save_path_data + 'logits_once_attacked.npy')
else:
#Evaluate the accuracy on adversarial examples
preds_adv = model.get_logits(adv_x)
logits_1, adv_img_1 = do_eval(adv_x, preds_adv, points_x_bar, points_y_bar,
'test once attacked')
np.save(os.path.join(save_path_data, 'images_once_attacked'), adv_img_1)
np.save(os.path.join(save_path_data, 'logits_once_attacked'), logits_1)
# counter attack
attack_params['max_iterations'] = 1024
if CONFIG.DATASET == 'moon':
out_alpha2 = compute_epsilons_balls_alpha(x_mesh, np.squeeze(x_test),
np.squeeze(adv_img_1), model_path,
CONFIG.SAVE_PATH)
attack_params['learning_rate'] = out_alpha2
attack_params['const_a_min'] = -1
attack_params['max_iterations'] = 2048
plot_data(np.squeeze(adv_img_1), logits_1, CONFIG.SAVE_PATH+'data_pred1.png', x_mesh,
logits_mesh)
adv_adv_x = cw.generate(x, **attack_params)
x_k = np.concatenate((points_x, adv_img_1), axis=0)
y_k = np.concatenate((points_y, logits_1), axis=0)
if os.path.isfile( save_path_data + 'images_twice_attacked.npy' ):
adv_img_2 = np.load(save_path_data + 'images_twice_attacked.npy')
logits_2 = np.load(save_path_data + 'logits_twice_attacked.npy')
else:
# Evaluate the accuracy on adversarial examples
preds_adv_adv = model.get_logits(adv_adv_x)
logits_2, adv_img_2 = do_eval(adv_adv_x, preds_adv_adv, x_k, y_k,
'test twice attacked')
np.save(os.path.join(save_path_data, 'images_twice_attacked'), adv_img_2)
np.save(os.path.join(save_path_data, 'logits_twice_attacked'), logits_2)
if CONFIG.DATASET == 'moon':
plot_data(np.squeeze(adv_img_2[:len_x]), logits_2[:len_x],
CONFIG.SAVE_PATH+'data_pred2.png', x_mesh, logits_mesh)
plot_data(np.squeeze(adv_img_2[len_x:]), logits_2[len_x:],
CONFIG.SAVE_PATH+'data_pred12.png', x_mesh, logits_mesh)
test_balls(np.squeeze(x_k), np.squeeze(adv_img_2), logits_0, logits_1, logits_2,
CONFIG.SAVE_PATH)
compute_returnees(logits_0[len_x:], logits_1, logits_2[len_x:], logits_0[:len_x],
logits_2[:len_x], CONFIG.SAVE_PATH)
if x_test.shape[-1] > 1:
num_axis=(1,2,3)
else:
num_axis=(1,2)
D_p = np.squeeze(np.sqrt(np.sum(np.square(points_x-adv_img_2[:len_x]), axis=num_axis)))
D_p_p = np.squeeze(np.sqrt(np.sum(np.square(adv_img_1-adv_img_2[len_x:]),
axis=num_axis)))
D_p_mod, D_p_p_mod = modify_D(D_p, D_p_p, logits_0[len_x:], logits_1, logits_2[len_x:],
logits_0[:len_x], logits_2[:len_x])
if D_p_mod != [] and D_p_p_mod != []:
plot_violins(D_p_mod, D_p_p_mod, CONFIG.SAVE_PATH)
threshold_evaluation(D_p_mod, D_p_p_mod, CONFIG.SAVE_PATH)
_ = compute_auroc(D_p_mod, D_p_p_mod, CONFIG.SAVE_PATH)
plot_results_models(len_x, CONFIG.DATASET, CONFIG.SAVE_PATH)
print('Time needed:', time.time()-start)
return report
def main(argv=None):
"""
CIFAR10 CleverHans tutorial
:return:
"""
# CIFAR10-specific dimensions
img_rows = 32
img_cols = 32
channels = 3
nb_classes = 10
# Set TF random seed to improve reproducibility
tf.set_random_seed(1234)
if not hasattr(backend, "tf"):
raise RuntimeError("This tutorial requires keras to be configured"
" to use the TensorFlow backend.")
# Image dimensions ordering should follow the Theano convention
if keras.backend.image_dim_ordering() != 'tf':
keras.backend.set_image_dim_ordering('tf')
print("INFO: '~/.keras/keras.json' sets 'image_dim_ordering' to "
"'th', temporarily setting to 'tf'")
# Create TF session and set as Keras backend session
sess = tf.Session()
keras.backend.set_session(sess)
set_log_level(logging.WARNING)
# Get CIFAR10 test data
X_train, Y_train, X_test, Y_test = data_cifar10()
assert Y_train.shape[1] == 10.
label_smooth = .1
Y_train = Y_train.clip(label_smooth / 9., 1. - label_smooth)
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, channels))
y = tf.placeholder(tf.float32, shape=(None, 10))
phase = tf.placeholder(tf.bool, name="phase")
model_path = FLAGS.model_path
targeted = True if FLAGS.targeted else False
binary = True if FLAGS.binary else False
scale = True if FLAGS.scale else False
learning_rate = FLAGS.learning_rate
nb_filters = FLAGS.nb_filters
batch_size = FLAGS.batch_size
nb_samples = FLAGS.nb_samples
nb_epochs = FLAGS.nb_epochs
delay = FLAGS.delay
eps = FLAGS.eps
adv = FLAGS.adv
attack = FLAGS.attack
attack_iterations = FLAGS.attack_iterations
save = False
train_from_scratch = False
if model_path is not None:
if os.path.exists(model_path):
# check for existing model in immediate subfolder
if any(f.endswith('.meta') for f in os.listdir(model_path)):
binary, scale, nb_filters, batch_size, learning_rate, nb_epochs, adv = parse_model_settings(
model_path)
train_from_scratch = False
else:
model_path = build_model_save_path(
model_path, binary, batch_size, nb_filters, learning_rate, nb_epochs, adv, delay, scale)
print(model_path)
save = True
train_from_scratch = True
else:
train_from_scratch = True # train from scratch, but don't save since no path given
if binary:
if scale:
from cleverhans_tutorials.tutorial_models import make_scaled_binary_cnn
model = make_scaled_binary_cnn(phase, 'bin_', input_shape=(
None, img_rows, img_cols, channels), nb_filters=nb_filters)
else:
from cleverhans_tutorials.tutorial_models import make_basic_binary_cnn
model = make_basic_binary_cnn(phase, 'bin_', input_shape=(
None, img_rows, img_cols, channels), nb_filters=nb_filters)
else:
from cleverhans_tutorials.tutorial_models import make_basic_cnn
model = make_basic_cnn(phase, 'fp_', input_shape=(
None, img_rows, img_cols, channels), nb_filters=nb_filters)
preds = model(x, reuse=False)
print("Defined TensorFlow model graph.")
rng = np.random.RandomState([2017, 8, 30])
def evaluate():
# Evaluate the accuracy of the CIFAR10 model on legitimate test
# examples
eval_params = {'batch_size': batch_size}
acc = model_eval(
sess, x, y, preds, X_test, Y_test, phase=phase, args=eval_params)
assert X_test.shape[0] == 10000, X_test.shape
print('Test accuracy on legitimate examples: %0.4f' % acc)
# Train an CIFAR10 model
train_params = {
'binary': binary,
'nb_epochs': nb_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate,
'loss_name': 'train loss',
'filename': 'model',
'reuse_global_step': False,
'train_scope': 'train',
'is_training': True
}
if adv:
from cleverhans.attacks import FastGradientMethod
fgsm = FastGradientMethod(model, back='tf', sess=sess)
fgsm_params = {'eps': eps, 'clip_min': 0., 'clip_max': 1.}
adv_x_train = fgsm.generate(x, phase, **fgsm_params)
preds_adv = model.get_probs(adv_x_train)
if train_from_scratch:
if save:
train_params.update({'log_dir': model_path})
if adv and delay > 0:
train_params.update({'nb_epochs': delay})
# do clean training for 'nb_epochs' or 'delay' epochs
model_train(sess, x, y, preds, X_train, Y_train, phase=phase,
evaluate=evaluate, args=train_params, save=save, rng=rng)
# optionally do additional adversarial training
if adv:
print("Adversarial training for %d epochs" % (nb_epochs - delay))
train_params.update({'nb_epochs': nb_epochs - delay})
train_params.update({'reuse_global_step': True})
model_train(sess, x, y, preds, X_train, Y_train, phase=phase,
predictions_adv=preds_adv, evaluate=evaluate, args=train_params,
save=save, rng=rng)
else:
tf_model_load(sess, model_path)
print('Restored model from %s' % model_path)
evaluate()
# Evaluate the accuracy of the CIFAR10 model on legitimate test examples
eval_params = {'batch_size': batch_size}
accuracy = model_eval(sess, x, y, preds, X_test, Y_test, phase=phase,
feed={phase: False}, args=eval_params)
print('Test accuracy on legitimate test examples: {0}'.format(accuracy))
###########################################################################
# Build dataset
###########################################################################
if targeted:
from cleverhans.utils import build_targeted_dataset
adv_inputs, true_labels, adv_ys = build_targeted_dataset(
X_test, Y_test, np.arange(nb_samples), nb_classes, img_rows, img_cols, channels)
else:
adv_inputs = X_test[:nb_samples]
###########################################################################
# Craft adversarial examples using generic approach
###########################################################################
if targeted:
att_batch_size = np.clip(
nb_samples * (nb_classes - 1), a_max=MAX_BATCH_SIZE, a_min=1)
nb_adv_per_sample = nb_classes - 1
yname = "y_target"
else:
att_batch_size = np.minimum(nb_samples, MAX_BATCH_SIZE)
nb_adv_per_sample = 1
adv_ys = None
yname = "y"
print('Crafting ' + str(nb_samples) + ' * ' + str(nb_adv_per_sample) +
' adversarial examples')
print("This could take some time ...")
if attack == ATTACK_CARLINI_WAGNER_L2:
from cleverhans.attacks import CarliniWagnerL2
attacker = CarliniWagnerL2(model, back='tf', sess=sess)
attack_params = {'binary_search_steps': 1,
'max_iterations': attack_iterations,
'learning_rate': 0.1,
'batch_size': att_batch_size,
'initial_const': 10,
}
elif attack == ATTACK_JSMA:
from cleverhans.attacks import SaliencyMapMethod
attacker = SaliencyMapMethod(model, back='tf', sess=sess)
attack_params = {'theta': 1., 'gamma': 0.1}
elif attack == ATTACK_FGSM:
from cleverhans.attacks import FastGradientMethod
attacker = FastGradientMethod(model, back='tf', sess=sess)
attack_params = {'eps': eps}
elif attack == ATTACK_MADRYETAL:
from cleverhans.attacks import MadryEtAl
attacker = MadryEtAl(model, back='tf', sess=sess)
attack_params = {'eps': eps, 'eps_iter': 0.01, 'nb_iter': nb_iter}
else:
print("Attack undefined")
sys.exit(1)
attack_params.update({yname: adv_ys, 'clip_min': 0., 'clip_max': 1.})
X_test_adv = attacker.generate_np(adv_inputs, phase, **attack_params)
'''
adv_x = attacker.generate(x, phase, **attack_params)
# Craft adversarial examples using Fast Gradient Sign Method (FGSM)
eval_params = {'batch_size': att_batch_size}
X_test_adv, = batch_eval(sess, [x], [adv_x], [adv_inputs], feed={
phase: False}, args=eval_params)
'''
if targeted:
assert X_test_adv.shape[0] == nb_samples * \
(nb_classes - 1), X_test_adv.shape
# Evaluate the accuracy of the CIFAR10 model on adversarial examples
print("Evaluating targeted results")
adv_accuracy = model_eval(sess, x, y, preds, X_test_adv, true_labels,
phase=phase, args=eval_params)
else:
assert X_test_adv.shape[0] == nb_samples, X_test_adv.shape
# Evaluate the accuracy of the CIFAR10 model on adversarial examples
print("Evaluating un-targeted results")
adv_accuracy = model_eval(sess, x, y, preds, X_test_adv, Y_test,
phase=phase, args=eval_params)
# Compute the number of adversarial examples that were successfully found
print('Test accuracy on adversarial examples {0:.4f}'.format(adv_accuracy))
# Compute the average distortion introduced by the algorithm
percent_perturbed = np.mean(np.sum((X_test_adv - adv_inputs)**2,
axis=(1, 2, 3))**.5)
print('Avg. L_2 norm of perturbations {0:.4f}'.format(percent_perturbed))
# Friendly output for pasting into spreadsheet
print('{0:.4f},'.format(accuracy))
print('{0:.4f},'.format(adv_accuracy))
print('{0:.4f},'.format(percent_perturbed))
sess.close()
'''
print("Repeating the process, using adversarial training")
def evaluate_2():
# Evaluate the accuracy of the adversarialy trained CIFAR10 model on
# legitimate test examples
eval_params = {'batch_size': batch_size}
accuracy = model_eval(sess, x, y, preds, X_test, Y_test,
phase=phase,
args=eval_params)
print('Test accuracy on legitimate test examples: ' + str(accuracy))
# Evaluate the accuracy of the adversarially trained CIFAR10 model on
# adversarial examples
accuracy_adv = model_eval(sess, x, y, preds_adv, X_test,
Y_test, phase=phase, args=eval_params)
print('Test accuracy on adversarial examples: ' + str(accuracy_adv))
# Perform adversarial training
train_params.update({'reuse_global_step': True})
model_train(sess, x, y, preds, X_train, Y_train, phase=phase,
predictions_adv=preds_adv, evaluate=evaluate_2,
args=train_params)
'''
'''
def main(argv=None):
tf.set_random_seed(1234)
sess = tf.Session()
keras.backend.set_session(sess)
X_train, Y_train, X_test, Y_test = data_lmrc()
Y_train = Y_train.clip(.1 / 9., 1. - .1)
x = tf.placeholder(tf.float32, shape=(None, 128, 128, 3))
y = tf.placeholder(tf.float32, shape=(None, 10))
model = cnn_model(img_rows=128, img_cols=128, channels=3)
predictions = model(x)
def evaluate():
eval_params = {'batch_size': FLAGS.batch_size}
accuracy = model_eval(sess,
x,
y,
predictions,
X_test,
Y_test,
args=eval_params)
print('Test accuracy on legitimate test examples: ' + str(accuracy))
train_params = {
'nb_epochs': FLAGS.nb_epochs,
'batch_size': FLAGS.batch_size,
'learning_rate': FLAGS.learning_rate,
'train_dir': FLAGS.train_dir,
'filename': FLAGS.filename
}
model_path = os.path.join(FLAGS.train_dir, FLAGS.filename)
if os.path.exists(model_path + ".meta"):
tf_model_load(sess, model_path)
else:
model_train(sess,
x,
y,
predictions,
X_train,
Y_train,
evaluate=evaluate,
args=train_params,
save=True)
wrap = KerasModelWrapper(model)
nb_classes = 10
targeted = False
nb_adv_per_sample = str(nb_classes - 1) if targeted else '1'
n_adv = 1000
cw = CarliniWagnerL2(model, back='tf', sess=sess)
adv_inputs = X_test[:n_adv]
adv_ys = None
yname = "y"
cw_params = {
'binary_search_steps': 1,
yname: adv_ys,
'max_iterations': 100,
'learning_rate': 0.1,
'batch_size': 10,
'initial_const': 10,
}
adv = cw.generate_np(adv_inputs, **cw_params)
#nFeatures = np.ndarray(shape=(n_adv,3))
#for i in range(0,n_adv):
# img = adv_inputs[i]
# fast = cv2.FastFeatureDetector_create()
# kp = fast.detect(img,None)
# nFeatures[i][0] = len(kp)
# img = adv[i]
# fast = cv2.FastFeatureDetector_create()
# kp = fast.detect(img,None)
# nFeatures[i][1] = len(kp)
# nFeatures[i][2] = int(np.argmax(Y_test[i]))
#print('Format: Mean(Std)')
#for i in range(0,10):
# pdb.set_trace()
# rows = np.where(nFeatures[:,2] == i)
# data = np.delete(nFeatures[rows], 2, 1)
# mean = np.mean(data, 0)
# std = np.std(data, 0)
# print('Class{0} : Original Image {1:.2f}({2:.2f}) Adversarial Image {3:.2f}({4:.2f})'.format(i, mean[0], std[0], mean[1], std[1]))
adv_den = np.zeros(adv.shape)
for i in range(0, n_adv):
img = adv[i] * 255
img = img.astype('uint8')
dst = cv2.fastNlMeansDenoisingColored(img, None, 10, 10, 7, 21)
dst = dst.astype('float32')
dst /= 255
adv_den[i] = dst
eval_params = {'batch_size': np.minimum(nb_classes, 10)}
original_accuracy = model_eval(sess,
x,
y,
predictions,
adv_inputs,
Y_test[:n_adv],
args=eval_params)
adv_accuracy = model_eval(sess,
x,
y,
predictions,
adv,
Y_test[:n_adv],
args=eval_params)
print('Accuracy on original images {0:.4f}'.format(original_accuracy))
print('Accuracy on adversarial images {0:.4f}'.format(adv_accuracy))
adv_accuracy = model_eval(sess,
x,
y,
predictions,
adv_den,
Y_test[:n_adv],
args=eval_params)
print(
'Accuracy on denoised adversarial images {0:.4f}'.format(adv_accuracy))
percent_perturbed = np.mean(
np.sum((adv - adv_inputs)**2, axis=(1, 2, 3))**.5)
print('Avg. L_2 norm of perturbations adversarial samples {0:.4f}'.format(
percent_perturbed))
percent_perturbed = np.mean(
np.sum((adv - adv_inputs)**2, axis=(1, 2, 3))**.5)
print('Avg. L_2 norm of denoised perturbations {0:.4f}'.format(
percent_perturbed))
sess.close()
def mnist_tutorial_cw(train_start=0,
train_end=60000,
test_start=0,
test_end=10000,
viz_enabled=True,
nb_epochs=6,
batch_size=128,
nb_classes=10,
source_samples=10,
learning_rate=0.001,
attack_iterations=100,
model_path=os.path.join("models", "mnist"),
targeted=True):
"""
MNIST tutorial for Carlini and Wagner's attack
:param train_start: index of first training set example
:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
:param viz_enabled: (boolean) activate plots of adversarial examples
:param nb_epochs: number of epochs to train model
:param batch_size: size of training batches
:param nb_classes: number of output classes
:param source_samples: number of test inputs to attack
:param learning_rate: learning rate for training
:param model_path: path to the model file
:param targeted: should we run a targeted attack? or untargeted?
:return: an AccuracyReport object
"""
# Object used to keep track of (and return) key accuracies
report = AccuracyReport()
# MNIST-specific dimensions
img_rows = 28
img_cols = 28
channels = 1
# Set TF random seed to improve reproducibility
tf.set_random_seed(1234)
# Create TF session
sess = tf.Session()
print("Created TensorFlow session.")
set_log_level(logging.DEBUG)
# Get MNIST test data
X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end)
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, channels))
y = tf.placeholder(tf.float32, shape=(None, nb_classes))
# Define TF model graph
model = make_basic_cnn()
preds = model.get_probs(x)
print("Defined TensorFlow model graph.")
###########################################################################
# Training the model using TensorFlow
###########################################################################
# Train an MNIST model
train_params = {
'nb_epochs': nb_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate,
'train_dir': os.path.join(*os.path.split(model_path)[:-1]),
'filename': os.path.split(model_path)[-1]
}
rng = np.random.RandomState([2017, 8, 30])
# check if we've trained before, and if we have, use that pre-trained model
if os.path.exists(model_path + ".meta"):
tf_model_load(sess, model_path)
else:
model_train(sess,
x,
y,
preds,
X_train,
Y_train,
args=train_params,
save=os.path.exists("models"),
rng=rng)
# Evaluate the accuracy of the MNIST model on legitimate test examples
eval_params = {'batch_size': batch_size}
accuracy = model_eval(sess, x, y, preds, X_test, Y_test, args=eval_params)
assert X_test.shape[0] == test_end - test_start, X_test.shape
print('Test accuracy on legitimate test examples: {0}'.format(accuracy))
s = []
for i in range(0, len(X_test), 1):
pred = sess.run(preds, {x: X_test[i:i + 1]})
print(pred)
print(Y_test[i:i + 1])
s.append(np.sort(pred)[0, -1] - np.sort(pred)[0, -2])
#Draw a histogram
def draw_hist(myList, Title, Xlabel, Ylabel):
plt.hist(myList,
np.arange(0, 1, 0.01),
normed=True,
stacked=True,
facecolor='blue')
plt.xlabel(Xlabel)
plt.ylabel(Ylabel)
plt.title(Title)
plt.show()
draw_hist(myList=s,
Title='legitimate',
Xlabel='difference between max and second largest',
Ylabel='Probability')
report.clean_train_clean_eval = accuracy
###########################################################################
# Craft adversarial examples using Carlini and Wagner's approach
###########################################################################
nb_adv_per_sample = str(nb_classes - 1) if targeted else '1'
print('Crafting ' + str(source_samples) + ' * ' + nb_adv_per_sample +
' adversarial examples')
print("This could take some time ...")
# Instantiate a CW attack object
cw = CarliniWagnerL2(model, back='tf', sess=sess)
if viz_enabled:
assert source_samples == nb_classes
idxs = [
np.where(np.argmax(Y_test, axis=1) == i)[0][0]
for i in range(nb_classes)
]
if targeted:
if viz_enabled:
# Initialize our array for grid visualization
grid_shape = (nb_classes, nb_classes, img_rows, img_cols, channels)
grid_viz_data = np.zeros(grid_shape, dtype='f')
adv_inputs = np.array([[instance] * nb_classes
for instance in X_test[idxs]],
dtype=np.float32)
else:
adv_inputs = np.array([[instance] * nb_classes
for instance in X_test[:source_samples]],
dtype=np.float32)
one_hot = np.zeros((nb_classes, nb_classes))
one_hot[np.arange(nb_classes), np.arange(nb_classes)] = 1
adv_inputs = adv_inputs.reshape(
(source_samples * nb_classes, img_rows, img_cols, 1))
adv_ys = np.array([one_hot] * source_samples,
dtype=np.float32).reshape(
(source_samples * nb_classes, nb_classes))
yname = "y_target"
else:
if viz_enabled:
# Initialize our array for grid visualization
grid_shape = (nb_classes, 2, img_rows, img_cols, channels)
grid_viz_data = np.zeros(grid_shape, dtype='f')
adv_inputs = X_test[idxs]
else:
adv_inputs = X_test[:source_samples]
adv_ys = None
yname = "y"
cw_params = {
'binary_search_steps': 1,
yname: adv_ys,
'max_iterations': attack_iterations,
'learning_rate': 0.1,
'batch_size':
source_samples * nb_classes if targeted else source_samples,
'initial_const': 10
}
adv = cw.generate_np(adv_inputs, **cw_params)
preds_adv = model.get_probs(adv)
pred = sess.run(preds_adv, {x: adv_inputs})
'''
s = []
for i in range(0,len(adv_inputs),1):
print(pred[i])
s.append((np.sort(pred[i])[-1])-(np.sort(pred[i])[-2]))
#Draw a histogram
def draw_hist(myList,Title,Xlabel,Ylabel):
plt.hist(myList,np.arange(0,1,0.01),normed=True,stacked=True,facecolor='red')
plt.xlabel(Xlabel)
plt.ylabel(Ylabel)
plt.title(Title)
plt.show()
draw_hist(myList=s,Title='adversarial',Xlabel='difference between max and second largest',
Ylabel='Probability')
'''
eval_params = {'batch_size': np.minimum(nb_classes, source_samples)}
if targeted:
adv_accuracy = model_eval(sess,
x,
y,
preds,
adv,
adv_ys,
args=eval_params)
else:
if viz_enabled:
adv_accuracy = 1 - \
model_eval(sess, x, y, preds, adv, Y_test[
idxs], args=eval_params)
else:
adv_accuracy = 1 - \
model_eval(sess, x, y, preds, adv, Y_test[
:source_samples], args=eval_params)
if viz_enabled:
for j in range(nb_classes):
if targeted:
for i in range(nb_classes):
grid_viz_data[i, j] = adv[i * nb_classes + j]
else:
grid_viz_data[j, 0] = adv_inputs[j]
grid_viz_data[j, 1] = adv[j]
print(grid_viz_data.shape)
print('--------------------------------------')
# Compute the number of adversarial examples that were successfully found
print('Avg. rate of successful adv. examples {0:.4f}'.format(adv_accuracy))
report.clean_train_adv_eval = 1. - adv_accuracy
# Compute the average distortion introduced by the algorithm
percent_perturbed = np.mean(
np.sum((adv - adv_inputs)**2, axis=(1, 2, 3))**.5)
print('Avg. L_2 norm of perturbations {0:.4f}'.format(percent_perturbed))
# Close TF session
sess.close()
return report
def mnist_attack(train_start=0,
train_end=60000,
test_start=0,
test_end=10000,
viz_enabled=True,
nb_epochs=6,
batch_size=128,
nb_filters=64,
nb_samples=10,
learning_rate=0.001,
eps=0.3,
attack=0,
attack_iterations=100,
model_path=None,
targeted=False,
binary=False,
scale=False,
rand=False,
debug=None,
test=False,
data_dir=None,
delay=0,
adv=0,
nb_iter=40):
"""
MNIST tutorial for generic attack
:param train_start: index of first training set example
:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
:param viz_enabled: (boolean) activate plots of adversarial examples
:param nb_epochs: number of epochs to train model
:param batch_size: size of training batches
:param nb_classes: number of output classes
:param nb_samples: number of test inputs to attack
:param learning_rate: learning rate for training
:param model_path: path to the model file
:param targeted: should we run a targeted attack? or untargeted?
:return: an AccuracyReport object
"""
# Object used to keep track of (and return) key accuracies
report = AccuracyReport()
# MNIST-specific dimensions
img_rows = 28
img_cols = 28
channels = 1
nb_classes = 10
# Set TF random seed to improve reproducibility
tf.set_random_seed(1237)
# Create TF session
sess = tf.Session()
print("Created TensorFlow session.")
if debug:
set_log_level(logging.DEBUG)
else:
set_log_level(logging.WARNING) # for running on sharcnet
# Get MNIST test data
X_train, Y_train, X_test, Y_test = data_mnist(datadir=data_dir,
train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end)
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, channels))
y = tf.placeholder(tf.float32, shape=(None, nb_classes))
phase = tf.placeholder(tf.bool, name='phase')
# for attempting to break unscaled network.
logits_scalar = tf.placeholder_with_default(INIT_T,
shape=(),
name="logits_temperature")
save = False
train_from_scratch = False
if model_path is not None:
if os.path.exists(model_path):
# check for existing model in immediate subfolder
if any(f.endswith('.meta') for f in os.listdir(model_path)):
binary, scale, nb_filters, batch_size, learning_rate, nb_epochs, adv = parse_model_settings(
model_path)
train_from_scratch = False
else:
model_path = build_model_save_path(model_path, binary,
batch_size, nb_filters,
learning_rate, nb_epochs,
adv, delay, scale)
print(model_path)
save = True
train_from_scratch = True
else:
train_from_scratch = True # train from scratch, but don't save since no path given
# Define TF model graph
if binary:
print('binary=True')
if scale:
print('scale=True')
if rand:
print('rand=True')
from cleverhans_tutorials.tutorial_models import make_scaled_binary_rand_cnn
model = make_scaled_binary_rand_cnn(
phase,
logits_scalar,
'binsc_',
input_shape=(None, img_rows, img_cols, channels),
nb_filters=nb_filters)
else:
from cleverhans_tutorials.tutorial_models import make_scaled_binary_cnn
model = make_scaled_binary_cnn(phase,
logits_scalar,
'binsc_',
input_shape=(None, img_rows,
img_cols,
channels),
nb_filters=nb_filters)
else:
from cleverhans_tutorials.tutorial_models import make_basic_binary_cnn
model = make_basic_binary_cnn(phase,
logits_scalar,
'bin_',
nb_filters=nb_filters)
else:
if rand:
print('rand=True')
from cleverhans_tutorials.tutorial_models import make_scaled_rand_cnn
model = make_scaled_rand_cnn(phase,
logits_scalar,
'fp_rand',
nb_filters=nb_filters)
else:
from cleverhans_tutorials.tutorial_models import make_basic_cnn
model = make_basic_cnn(phase,
logits_scalar,
'fp_',
nb_filters=nb_filters)
preds = model(x, reuse=False) # * logits_scalar
print("Defined TensorFlow model graph.")
###########################################################################
# Training the model using TensorFlow
###########################################################################
rng = np.random.RandomState([2017, 8, 30])
# Train an MNIST model
train_params = {
'binary': binary,
'nb_epochs': nb_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate,
'loss_name': 'train loss',
'filename': 'model',
'reuse_global_step': False,
'train_scope': 'train',
'is_training': True
}
if adv != 0:
if adv == ADVERSARIAL_TRAINING_MADRYETAL:
from cleverhans.attacks import MadryEtAl
train_attack_params = {
'eps': MAX_EPS,
'eps_iter': 0.01,
'nb_iter': nb_iter
}
train_attacker = MadryEtAl(model, sess=sess)
elif adv == ADVERSARIAL_TRAINING_FGSM:
from cleverhans.attacks import FastGradientMethod
stddev = int(np.ceil((MAX_EPS * 255) // 2))
train_attack_params = {
'eps':
tf.abs(
tf.truncated_normal(shape=(batch_size, 1, 1, 1),
mean=0,
stddev=stddev))
}
train_attacker = FastGradientMethod(model, back='tf', sess=sess)
# create the adversarial trainer
train_attack_params.update({'clip_min': 0., 'clip_max': 1.})
adv_x_train = train_attacker.generate(x, phase, **train_attack_params)
preds_adv_train = model.get_probs(adv_x_train)
eval_attack_params = {'eps': MAX_EPS, 'clip_min': 0., 'clip_max': 1.}
adv_x_eval = train_attacker.generate(x, phase, **eval_attack_params)
preds_adv_eval = model.get_probs(adv_x_eval) # * logits_scalar
def evaluate():
# Evaluate the accuracy of the MNIST model on clean test examples
eval_params = {'batch_size': batch_size}
acc = model_eval(sess,
x,
y,
preds,
X_test,
Y_test,
phase=phase,
args=eval_params)
report.clean_train_clean_eval = acc
assert X_test.shape[0] == test_end - test_start, X_test.shape
print('Test accuracy on legitimate examples: %0.4f' % acc)
if adv != 0:
# Accuracy of the adversarially trained model on adversarial
# examples
acc = model_eval(sess,
x,
y,
preds_adv_eval,
X_test,
Y_test,
phase=phase,
args=eval_params)
print('Test accuracy on adversarial examples: %0.4f' % acc)
acc = model_eval(sess,
x,
y,
preds_adv_eval,
X_test,
Y_test,
phase=phase,
args=eval_params,
feed={logits_scalar: ATTACK_T})
print('Test accuracy on adversarial examples (scaled): %0.4f' %
acc)
if train_from_scratch:
if save:
train_params.update({'log_dir': model_path})
if adv and delay > 0:
train_params.update({'nb_epochs': delay})
# do clean training for 'nb_epochs' or 'delay' epochs
if test:
model_train(sess,
x,
y,
preds,
X_train,
Y_train,
phase=phase,
evaluate=evaluate,
args=train_params,
save=save,
rng=rng)
else:
model_train(sess,
x,
y,
preds,
X_train,
Y_train,
phase=phase,
args=train_params,
save=save,
rng=rng)
# optionally do additional adversarial training
if adv:
print("Adversarial training for %d epochs" % (nb_epochs - delay))
train_params.update({'nb_epochs': nb_epochs - delay})
train_params.update({'reuse_global_step': True})
if test:
model_train(sess,
x,
y,
preds,
X_train,
Y_train,
phase=phase,
predictions_adv=preds_adv_train,
evaluate=evaluate,
args=train_params,
save=save,
rng=rng)
else:
model_train(sess,
x,
y,
preds,
X_train,
Y_train,
phase=phase,
predictions_adv=preds_adv_train,
args=train_params,
save=save,
rng=rng)
else:
tf_model_load(sess, model_path)
print('Restored model from %s' % model_path)
evaluate()
# Evaluate the accuracy of the MNIST model on legitimate test examples
eval_params = {'batch_size': batch_size}
accuracy = model_eval(sess,
x,
y,
preds,
X_test,
Y_test,
phase=phase,
feed={phase: False},
args=eval_params)
assert X_test.shape[0] == test_end - test_start, X_test.shape
print('Test accuracy on legitimate test examples: {0}'.format(accuracy))
report.clean_train_clean_eval = accuracy
###########################################################################
# Build dataset
###########################################################################
if viz_enabled:
assert nb_samples == nb_classes
idxs = [
np.where(np.argmax(Y_test, axis=1) == i)[0][0]
for i in range(nb_classes)
]
viz_rows = nb_classes if targeted else 2
# Initialize our array for grid visualization
grid_shape = (nb_classes, viz_rows, img_rows, img_cols, channels)
grid_viz_data = np.zeros(grid_shape, dtype='f')
if targeted:
from cleverhans.utils import build_targeted_dataset
if viz_enabled:
from cleverhans.utils import grid_visual
adv_inputs, true_labels, adv_ys = build_targeted_dataset(
X_test, Y_test, idxs, nb_classes, img_rows, img_cols, channels)
else:
adv_inputs, true_labels, adv_ys = build_targeted_dataset(
X_test, Y_test, np.arange(nb_samples), nb_classes, img_rows,
img_cols, channels)
else:
if viz_enabled:
from cleverhans.utils import pair_visual
adv_inputs = X_test[idxs]
else:
adv_inputs = X_test[:nb_samples]
###########################################################################
# Craft adversarial examples using generic approach
###########################################################################
if targeted:
att_batch_size = np.clip(nb_samples * (nb_classes - 1),
a_max=MAX_BATCH_SIZE,
a_min=1)
nb_adv_per_sample = nb_classes - 1
yname = "y_target"
else:
att_batch_size = np.minimum(nb_samples, MAX_BATCH_SIZE)
nb_adv_per_sample = 1
adv_ys = None
yname = "y"
print('Crafting ' + str(nb_samples) + ' * ' + str(nb_adv_per_sample) +
' adversarial examples')
print("This could take some time ...")
if attack == ATTACK_CARLINI_WAGNER_L2:
print('Attack: CarliniWagnerL2')
from cleverhans.attacks import CarliniWagnerL2
attacker = CarliniWagnerL2(model, back='tf', sess=sess)
attack_params = {
'binary_search_steps': 1,
'max_iterations': attack_iterations,
'learning_rate': 0.1,
'batch_size': att_batch_size,
'initial_const': 10,
}
elif attack == ATTACK_JSMA:
print('Attack: SaliencyMapMethod')
from cleverhans.attacks import SaliencyMapMethod
attacker = SaliencyMapMethod(model, back='tf', sess=sess)
attack_params = {'theta': 1., 'gamma': 0.1}
elif attack == ATTACK_FGSM:
print('Attack: FastGradientMethod')
from cleverhans.attacks import FastGradientMethod
attacker = FastGradientMethod(model, back='tf', sess=sess)
attack_params = {'eps': eps}
elif attack == ATTACK_MADRYETAL:
print('Attack: MadryEtAl')
from cleverhans.attacks import MadryEtAl
attacker = MadryEtAl(model, back='tf', sess=sess)
attack_params = {'eps': eps, 'eps_iter': 0.01, 'nb_iter': nb_iter}
elif attack == ATTACK_BASICITER:
print('Attack: BasicIterativeMethod')
from cleverhans.attacks import BasicIterativeMethod
attacker = BasicIterativeMethod(model, back='tf', sess=sess)
attack_params = {'eps': eps, 'eps_iter': 0.01, 'nb_iter': nb_iter}
else:
print("Attack undefined")
sys.exit(1)
attack_params.update({yname: adv_ys, 'clip_min': 0., 'clip_max': 1.})
adv_np = attacker.generate_np(adv_inputs, phase, **attack_params)
'''
name = 'm_fgsm_eps%s_n%s.npy' % (eps, nb_samples)
fpath = os.path.join(
'/scratch/gallowaa/mnist/adversarial_examples/cleverhans/', name)
np.savez(fpath, x=adv_np, y=Y_test[:nb_samples])
'''
'''
adv_x = attacker.generate(x, phase, **attack_params)
adv_np, = batch_eval(sess, [x], [adv_x], [adv_inputs], feed={
phase: False}, args=eval_params)
'''
eval_params = {'batch_size': att_batch_size}
if targeted:
print("Evaluating targeted results")
adv_accuracy = model_eval(sess,
x,
y,
preds,
adv_np,
true_labels,
phase=phase,
args=eval_params)
else:
print("Evaluating untargeted results")
if viz_enabled:
adv_accuracy = model_eval(sess,
x,
y,
preds,
adv_np,
Y_test[idxs],
phase=phase,
args=eval_params)
else:
adv_accuracy = model_eval(sess,
x,
y,
preds,
adv_np,
Y_test[:nb_samples],
phase=phase,
args=eval_params)
if viz_enabled:
n = nb_classes - 1
for i in range(nb_classes):
if targeted:
for j in range(nb_classes):
if i != j:
if j != 0 and i != n:
grid_viz_data[i, j] = adv_np[j * n + i]
if j == 0 and i > 0 or i == n and j > 0:
grid_viz_data[i, j] = adv_np[j * n + i - 1]
else:
grid_viz_data[i, j] = adv_inputs[j * n]
else:
grid_viz_data[j, 0] = adv_inputs[j]
grid_viz_data[j, 1] = adv_np[j]
print(grid_viz_data.shape)
print('--------------------------------------')
# Compute the number of adversarial examples that were successfully found
print('Test accuracy on adversarial examples {0:.4f}'.format(adv_accuracy))
report.clean_train_adv_eval = 1. - adv_accuracy
# Compute the average distortion introduced by the algorithm
percent_perturbed = np.mean(
np.sum((adv_np - adv_inputs)**2, axis=(1, 2, 3))**.5)
print('Avg. L_2 norm of perturbations {0:.4f}'.format(percent_perturbed))
# Compute number of modified features (L_0 norm)
nb_changed = np.where(adv_np != adv_inputs)[0].shape[0]
percent_perturb = np.mean(float(nb_changed) / adv_np.reshape(-1).shape[0])
# Compute the average distortion introduced by the algorithm
print('Avg. rate of perturbed features {0:.4f}'.format(percent_perturb))
# Friendly output for pasting into spreadsheet
print('{0:.4f}'.format(accuracy))
print('{0:.4f}'.format(adv_accuracy))
print('{0:.4f}'.format(percent_perturbed))
print('{0:.4f}'.format(percent_perturb))
# Close TF session
sess.close()
# Finally, block & display a grid of all the adversarial examples
if viz_enabled:
import matplotlib.pyplot as plt
_ = grid_visual(grid_viz_data)
return report
def main(argv=None):
"""
CIFAR10 CleverHans tutorial
:return:
"""
# CIFAR10-specific dimensions
img_rows = 32
img_cols = 32
channels = 3
nb_classes = 10
# Set TF random seed to improve reproducibility
tf.set_random_seed(1234)
if not hasattr(backend, "tf"):
raise RuntimeError("This tutorial requires keras to be configured"
" to use the TensorFlow backend.")
# Image dimensions ordering should follow the Theano convention
if keras.backend.image_dim_ordering() != 'tf':
keras.backend.set_image_dim_ordering('tf')
print("INFO: '~/.keras/keras.json' sets 'image_dim_ordering' to "
"'th', temporarily setting to 'tf'")
# Create TF session and set as Keras backend session
sess = tf.Session()
keras.backend.set_session(sess)
set_log_level(logging.WARNING)
# Get CIFAR10 test data
X_train, Y_train, X_test, Y_test = data_cifar10()
assert Y_train.shape[1] == 10.
label_smooth = .1
Y_train = Y_train.clip(label_smooth / 9., 1. - label_smooth)
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, channels))
y = tf.placeholder(tf.float32, shape=(None, 10))
phase = tf.placeholder(tf.bool, name="phase")
logits_scalar = tf.placeholder_with_default(INIT_T,
shape=(),
name="logits_temperature")
model_path = FLAGS.model_path
targeted = True if FLAGS.targeted else False
learning_rate = FLAGS.learning_rate
nb_filters = FLAGS.nb_filters
batch_size = FLAGS.batch_size
nb_samples = FLAGS.nb_samples
nb_epochs = FLAGS.nb_epochs
delay = FLAGS.delay
eps = FLAGS.eps
adv = FLAGS.adv
attack = FLAGS.attack
attack_iterations = FLAGS.attack_iterations
nb_iter = FLAGS.nb_iter
#### EMPIR extra flags
lowprecision = FLAGS.lowprecision
abits = FLAGS.abits
wbits = FLAGS.wbits
abitsList = FLAGS.abitsList
wbitsList = FLAGS.wbitsList
stocRound = True if FLAGS.stocRound else False
rand = FLAGS.rand
model_path2 = FLAGS.model_path2
model_path1 = FLAGS.model_path1
model_path3 = FLAGS.model_path3
ensembleThree = True if FLAGS.ensembleThree else False
abits2 = FLAGS.abits2
wbits2 = FLAGS.wbits2
abits2List = FLAGS.abits2List
wbits2List = FLAGS.wbits2List
inpgradreg = True if FLAGS.inpgradreg else False
distill = True if FLAGS.distill else False
student_epochs = FLAGS.student_epochs
l2dbl = FLAGS.l2dbl
l2cs = FLAGS.l2cs
####
save = False
train_from_scratch = False
if ensembleThree:
if (model_path1 is None or model_path2 is None or model_path3 is None):
train_from_scratch = True
else:
train_from_scratch = False
elif model_path is not None:
if os.path.exists(model_path):
# check for existing model in immediate subfolder
if any(f.endswith('.meta') for f in os.listdir(model_path)):
train_from_scratch = False
else:
model_path = build_model_save_path(model_path, batch_size,
nb_filters, learning_rate,
nb_epochs, adv, delay)
print(model_path)
save = True
train_from_scratch = True
else:
train_from_scratch = True # train from scratch, but don't save since no path given
if ensembleThree:
if (wbitsList is None) or (
abitsList is None
): # Layer wise separate quantization not specified for first model
if (wbits == 0) or (abits == 0):
print(
"Error: the number of bits for constant precision weights and activations across layers for the first model have to specified using wbits1 and abits1 flags"
)
sys.exit(1)
else:
fixedPrec1 = 1
elif (len(wbitsList) != 3) or (len(abitsList) != 3):
print(
"Error: Need to specify the precisions for activations and weights for the atleast the three convolutional layers of the first model"
)
sys.exit(1)
else:
fixedPrec1 = 0
if (wbits2List is None) or (
abits2List is None
): # Layer wise separate quantization not specified for second model
if (wbits2 == 0) or (abits2 == 0):
print(
"Error: the number of bits for constant precision weights and activations across layers for the second model have to specified using wbits1 and abits1 flags"
)
sys.exit(1)
else:
fixedPrec2 = 1
elif (len(wbits2List) != 3) or (len(abits2List) != 3):
print(
"Error: Need to specify the precisions for activations and weights for the atleast the three convolutional layers of the second model"
)
sys.exit(1)
else:
fixedPrec2 = 0
if (fixedPrec2 != 1) or (
fixedPrec1 != 1
): # Atleast one of the models have separate precisions per layer
fixedPrec = 0
print("Within atleast one model has separate precisions")
if (fixedPrec1 == 1): # first layer has fixed precision
abitsList = (abits, abits, abits)
wbitsList = (wbits, wbits, wbits)
if (fixedPrec2 == 1): # second layer has fixed precision
abits2List = (abits2, abits2, abits2)
wbits2List = (wbits2, wbits2, wbits2)
else:
fixedPrec = 1
if (train_from_scratch):
print("The ensemble model cannot be trained from scratch")
sys.exit(1)
if fixedPrec == 1:
from cleverhans_tutorials.tutorial_models import make_ensemble_three_cifar_cnn
model = make_ensemble_three_cifar_cnn(phase,
logits_scalar,
'lp1_',
'lp2_',
'fp_',
wbits,
abits,
wbits2,
abits2,
input_shape=(None, img_rows,
img_cols,
channels),
nb_filters=nb_filters)
else:
from cleverhans_tutorials.tutorial_models import make_ensemble_three_cifar_cnn_layerwise
model = make_ensemble_three_cifar_cnn_layerwise(
phase,
logits_scalar,
'lp1_',
'lp2_',
'fp_',
wbitsList,
abitsList,
wbits2List,
abits2List,
input_shape=(None, img_rows, img_cols, channels),
nb_filters=nb_filters)
elif lowprecision:
if (wbitsList is None) or (
abitsList is
None): # Layer wise separate quantization not specified
if (wbits == 0) or (abits == 0):
print(
"Error: the number of bits for constant precision weights and activations across layers have to specified using wbits and abits flags"
)
sys.exit(1)
else:
fixedPrec = 1
elif (len(wbitsList) != 3) or (len(abitsList) != 3):
print(
"Error: Need to specify the precisions for activations and weights for the atleast the three convolutional layers"
)
sys.exit(1)
else:
fixedPrec = 0
if fixedPrec:
from cleverhans_tutorials.tutorial_models import make_basic_lowprecision_cifar_cnn
model = make_basic_lowprecision_cifar_cnn(
phase,
logits_scalar,
'lp_',
wbits,
abits,
input_shape=(None, img_rows, img_cols, channels),
nb_filters=nb_filters,
stocRound=stocRound)
else:
from cleverhans_tutorials.tutorial_models import make_layerwise_lowprecision_cifar_cnn
model = make_layerwise_lowprecision_cifar_cnn(
phase,
logits_scalar,
'lp_',
wbitsList,
abitsList,
input_shape=(None, img_rows, img_cols, channels),
nb_filters=nb_filters,
stocRound=stocRound)
elif distill:
from cleverhans_tutorials.tutorial_models import make_distilled_cifar_cnn
model = make_distilled_cifar_cnn(phase,
logits_scalar,
'teacher_fp_',
'fp_',
nb_filters=nb_filters,
input_shape=(None, img_rows, img_cols,
channels))
####
else:
from cleverhans_tutorials.tutorial_models import make_basic_cifar_cnn
model = make_basic_cifar_cnn(phase,
logits_scalar,
'fp_',
input_shape=(None, img_rows, img_cols,
channels),
nb_filters=nb_filters)
# separate predictions of teacher for distilled training
if distill:
teacher_preds = model.teacher_call(x, reuse=False)
teacher_logits = model.get_teacher_logits(x, reuse=False)
# separate calling function for ensemble models
if ensembleThree:
preds = model.ensemble_call(x, reuse=False)
else:
##default
preds = model(x, reuse=False)
print("Defined TensorFlow model graph.")
rng = np.random.RandomState([2017, 8, 30])
def evaluate():
# Evaluate the accuracy of the CIFAR10 model on legitimate test
# examples
eval_params = {'batch_size': batch_size}
if ensembleThree:
acc = model_eval_ensemble(sess,
x,
y,
preds,
X_test,
Y_test,
phase=phase,
args=eval_params)
else:
acc = model_eval(sess,
x,
y,
preds,
X_test,
Y_test,
phase=phase,
args=eval_params)
assert X_test.shape[0] == 10000, X_test.shape
print('Test accuracy on legitimate examples: %0.4f' % acc)
# Train an CIFAR10 model
train_params = {
'nb_epochs': nb_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate,
'loss_name': 'train loss',
'filename': 'model',
'reuse_global_step': False,
'train_scope': 'train',
'is_training': True
}
if adv != 0:
if adv == ADVERSARIAL_TRAINING_MADRYETAL:
from cleverhans.attacks import MadryEtAl
train_attack_params = {
'eps': MAX_EPS,
'eps_iter': 0.01,
'nb_iter': nb_iter
}
train_attacker = MadryEtAl(model, sess=sess)
elif adv == ADVERSARIAL_TRAINING_FGSM:
from cleverhans.attacks import FastGradientMethod
stddev = int(np.ceil((MAX_EPS * 255) // 2))
train_attack_params = {
'eps':
tf.abs(
tf.truncated_normal(shape=(batch_size, 1, 1, 1),
mean=0,
stddev=stddev))
}
train_attacker = FastGradientMethod(model, back='tf', sess=sess)
# create the adversarial trainer
train_attack_params.update({'clip_min': 0., 'clip_max': 1.})
adv_x_train = train_attacker.generate(x, phase, **train_attack_params)
preds_adv_train = model.get_probs(adv_x_train)
eval_attack_params = {'eps': MAX_EPS, 'clip_min': 0., 'clip_max': 1.}
adv_x_eval = train_attacker.generate(x, phase, **eval_attack_params)
preds_adv_eval = model.get_probs(adv_x_eval) # * logits_scalar
if train_from_scratch:
if save:
train_params.update({'log_dir': model_path})
if adv and delay > 0:
train_params.update({'nb_epochs': delay})
# do clean training for 'nb_epochs' or 'delay' epochs
if distill:
temperature = 10 # 1 means the teacher predictions are used as it is
teacher_scaled_preds_val = model_train_teacher(sess,
x,
y,
teacher_preds,
teacher_logits,
temperature,
X_train,
Y_train,
phase=phase,
args=train_params,
rng=rng)
eval_params = {'batch_size': batch_size}
teacher_acc = model_eval(sess,
x,
y,
teacher_preds,
X_test,
Y_test,
phase=phase,
args=eval_params)
print(
'Test accuracy of the teacher model on legitimate examples: %0.4f'
% teacher_acc)
print('Training the student model...')
student_train_params = {
'nb_epochs': student_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate,
'loss_name': 'train loss',
'filename': 'model',
'reuse_global_step': False,
'train_scope': 'train',
'is_training': True
}
if save:
student_train_params.update({'log_dir': model_path})
y_teacher = tf.placeholder(tf.float32, shape=(None, nb_classes))
model_train_student(sess,
x,
y,
preds,
temperature,
X_train,
Y_train,
y_teacher=y_teacher,
teacher_preds=teacher_scaled_preds_val,
alpha=0.3,
beta=0.7,
phase=phase,
evaluate=evaluate,
args=student_train_params,
save=save,
rng=rng)
elif inpgradreg:
model_train_inpgrad_reg(sess,
x,
y,
preds,
X_train,
Y_train,
phase=phase,
evaluate=evaluate,
l2dbl=l2dbl,
l2cs=l2cs,
args=train_params,
save=save,
rng=rng)
else:
# do clean training for 'nb_epochs' or 'delay' epochs
model_train(sess,
x,
y,
preds,
X_train,
Y_train,
phase=phase,
evaluate=evaluate,
args=train_params,
save=save,
rng=rng)
# optionally do additional adversarial training
if adv:
print("Adversarial training for %d epochs" % (nb_epochs - delay))
train_params.update({'nb_epochs': nb_epochs - delay})
train_params.update({'reuse_global_step': True})
model_train(sess,
x,
y,
preds,
X_train,
Y_train,
phase=phase,
predictions_adv=preds_adv_train,
evaluate=evaluate,
args=train_params,
save=save,
rng=rng)
else:
if ensembleThree:
variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
stored_variables = [
'lp_conv1_init/k', 'lp_conv2_init/k', 'lp_conv3_init/k',
'lp_ip1init/W', 'lp_logits_init/W'
]
variable_dict = dict(zip(stored_variables, variables[:5]))
# Restore the first set of variables from model_path1
saver = tf.train.Saver(variable_dict)
saver.restore(sess, tf.train.latest_checkpoint(model_path1))
# Restore the second set of variables from model_path2
variable_dict = dict(zip(stored_variables, variables[5:10]))
saver2 = tf.train.Saver(variable_dict)
saver2.restore(sess, tf.train.latest_checkpoint(model_path2))
stored_variables = [
'fp_conv1_init/k', 'fp_conv2_init/k', 'fp_conv3_init/k',
'fp_ip1init/W', 'fp_logits_init/W'
]
variable_dict = dict(zip(stored_variables, variables[10:]))
saver3 = tf.train.Saver(variable_dict)
saver3.restore(sess, tf.train.latest_checkpoint(model_path3))
else:
tf_model_load(sess, model_path)
print('Restored model from %s' % model_path)
evaluate()
# Evaluate the accuracy of the CIFAR10 model on legitimate test examples
eval_params = {'batch_size': batch_size}
if ensembleThree:
accuracy = model_eval_ensemble(sess,
x,
y,
preds,
X_test,
Y_test,
phase=phase,
feed={phase: False},
args=eval_params)
else:
accuracy = model_eval(sess,
x,
y,
preds,
X_test,
Y_test,
phase=phase,
feed={phase: False},
args=eval_params)
print('Test accuracy on legitimate test examples: {0}'.format(accuracy))
###########################################################################
# Build dataset
###########################################################################
if targeted:
from cleverhans.utils import build_targeted_dataset
adv_inputs, true_labels, adv_ys = build_targeted_dataset(
X_test, Y_test, np.arange(nb_samples), nb_classes, img_rows,
img_cols, channels)
else:
adv_inputs = X_test[:nb_samples]
###########################################################################
# Craft adversarial examples using generic approach
###########################################################################
if targeted:
att_batch_size = np.clip(nb_samples * (nb_classes - 1),
a_max=MAX_BATCH_SIZE,
a_min=1)
nb_adv_per_sample = nb_classes - 1
yname = "y_target"
else:
att_batch_size = np.minimum(nb_samples, MAX_BATCH_SIZE)
nb_adv_per_sample = 1
adv_ys = None
yname = "y"
print('Crafting ' + str(nb_samples) + ' * ' + str(nb_adv_per_sample) +
' adversarial examples')
print("This could take some time ...")
if ensembleThree:
model_type = 'ensembleThree'
else:
model_type = 'default'
if attack == ATTACK_CARLINI_WAGNER_L2:
from cleverhans.attacks import CarliniWagnerL2
attacker = CarliniWagnerL2(model,
back='tf',
model_type=model_type,
num_classes=nb_classes,
sess=sess)
attack_params = {
'binary_search_steps': 1,
'max_iterations': attack_iterations,
'learning_rate': 0.1,
'batch_size': att_batch_size,
'initial_const': 10,
}
elif attack == ATTACK_JSMA:
from cleverhans.attacks import SaliencyMapMethod
attacker = SaliencyMapMethod(model,
back='tf',
model_type=model_type,
sess=sess,
num_classes=nb_classes)
attack_params = {'theta': 1., 'gamma': 0.1}
elif attack == ATTACK_FGSM:
from cleverhans.attacks import FastGradientMethod
attacker = FastGradientMethod(model,
back='tf',
model_type=model_type,
sess=sess,
num_classes=nb_classes)
attack_params = {'eps': eps}
elif attack == ATTACK_MADRYETAL:
from cleverhans.attacks import MadryEtAl
attacker = MadryEtAl(model,
back='tf',
model_type=model_type,
sess=sess,
num_classes=nb_classes)
attack_params = {'eps': eps, 'eps_iter': 0.01, 'nb_iter': nb_iter}
elif attack == ATTACK_BASICITER:
from cleverhans.attacks import BasicIterativeMethod
attacker = BasicIterativeMethod(model,
back='tf',
sess=sess,
model_type=model_type,
num_classes=nb_classes)
attack_params = {'eps': eps, 'eps_iter': 0.01, 'nb_iter': nb_iter}
else:
print("Attack undefined")
sys.exit(1)
attack_params.update({yname: adv_ys, 'clip_min': 0., 'clip_max': 1.})
X_test_adv = attacker.generate_np(adv_inputs, phase, **attack_params)
'''
adv_x = attacker.generate(x, phase, **attack_params)
# Craft adversarial examples using Fast Gradient Sign Method (FGSM)
eval_params = {'batch_size': att_batch_size}
X_test_adv, = batch_eval(sess, [x], [adv_x], [adv_inputs], feed={
phase: False}, args=eval_params)
'''
if targeted:
assert X_test_adv.shape[0] == nb_samples * \
(nb_classes - 1), X_test_adv.shape
# Evaluate the accuracy of the CIFAR10 model on adversarial examples
print("Evaluating targeted results")
adv_accuracy = model_eval(sess,
x,
y,
preds,
X_test_adv,
true_labels,
phase=phase,
args=eval_params)
else:
# assert X_test_adv.shape[0] == nb_samples, X_test_adv.shape
# Evaluate the accuracy of the CIFAR10 model on adversarial examples
print("Evaluating un-targeted results")
if ensembleThree:
adv_accuracy = model_eval_ensemble(sess,
x,
y,
preds,
X_test_adv,
Y_test,
phase=phase,
args=eval_params)
else: #default below
adv_accuracy = model_eval(sess,
x,
y,
preds,
X_test_adv,
Y_test,
phase=phase,
args=eval_params)
# Compute the number of adversarial examples that were successfully found
print('Test accuracy on adversarial examples {0:.4f}'.format(adv_accuracy))
# Compute the average distortion introduced by the algorithm
percent_perturbed = np.mean(
np.sum((X_test_adv - adv_inputs)**2, axis=(1, 2, 3))**.5)
print('Avg. L_2 norm of perturbations {0:.4f}'.format(percent_perturbed))
# Friendly output for pasting into spreadsheet
print('{0:.4f},'.format(accuracy))
print('{0:.4f},'.format(adv_accuracy))
print('{0:.4f},'.format(percent_perturbed))
sess.close()
'''
print("Repeating the process, using adversarial training")
def evaluate_2():
# Evaluate the accuracy of the adversarialy trained CIFAR10 model on
# legitimate test examples
eval_params = {'batch_size': batch_size}
accuracy = model_eval(sess, x, y, preds, X_test, Y_test,
phase=phase,
args=eval_params)
print('Test accuracy on legitimate test examples: ' + str(accuracy))
# Evaluate the accuracy of the adversarially trained CIFAR10 model on
# adversarial examples
accuracy_adv = model_eval(sess, x, y, preds_adv, X_test,
Y_test, phase=phase, args=eval_params)
print('Test accuracy on adversarial examples: ' + str(accuracy_adv))
# Perform adversarial training
train_params.update({'reuse_global_step': True})
model_train(sess, x, y, preds, X_train, Y_train, phase=phase,
predictions_adv=preds_adv, evaluate=evaluate_2,
args=train_params)
'''
'''
def restore(self, path):
"""
Wrapper around cleverhans utils tf.model_load
"""
return tf_model_load(self.session, path)
def mnist_tutorial_fgsm(train_start=0, train_end=60000, test_start=0,
test_end=10000, viz_enabled=VIZ_ENABLED,
nb_epochs=NB_EPOCHS, batch_size=BATCH_SIZE,
source_samples=SOURCE_SAMPLES,
learning_rate=LEARNING_RATE,
attack_iterations=ATTACK_ITERATIONS,
model_path=MODEL_PATH,
targeted=TARGETED,
noise_output=NOISE_OUTPUT):
"""
MNIST tutorial for Fast Gradient Method's attack
:param train_start: index of first training set example
:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
:param viz_enabled: (boolean) activate plots of adversarial examples
:param nb_epochs: number of epochs to train model
:param batch_size: size of training batches
:param nb_classes: number of output classes
:param source_samples: number of test inputs to attack
:param learning_rate: learning rate for training
:param model_path: path to the model file
:param targeted: should we run a targeted attack? or untargeted?
:return: an AccuracyReport object
"""
# Object used to keep track of (and return) key accuracies
report = AccuracyReport()
# Set TF random seed to improve reproducibility
tf.set_random_seed(1234)
# Create TF session
sess = tf.Session()
print("Created TensorFlow session.")
set_log_level(logging.DEBUG)
# Get MNIST test data
mnist = MNIST(train_start=train_start, train_end=train_end,
test_start=test_start, test_end=test_end)
x_train, y_train = mnist.get_set('train')
x_test, y_test = mnist.get_set('test')
# Obtain Image Parameters
img_rows, img_cols, nchannels = x_train.shape[1:4]
nb_classes = y_train.shape[1]
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols,
nchannels))
y = tf.placeholder(tf.float32, shape=(None, nb_classes))
nb_filters = 64
# Define TF model graph
model = ModelBasicCNN('model1', nb_classes, nb_filters)
preds = model.get_logits(x)
loss = CrossEntropy(model, smoothing=0.1)
print("Defined TensorFlow model graph.")
###########################################################################
# Training the model using TensorFlow
###########################################################################
# Train an MNIST model
train_params = {
'nb_epochs': nb_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate,
'filename': os.path.split(model_path)[-1]
}
rng = np.random.RandomState([2017, 8, 30])
# check if we've trained before, and if we have, use that pre-trained model
if os.path.exists(model_path + ".meta"):
tf_model_load(sess, model_path)
else:
train(sess, loss, x_train, y_train, args=train_params, rng=rng)
saver = tf.train.Saver()
saver.save(sess, model_path)
# Evaluate the accuracy of the MNIST model on legitimate test examples
eval_params = {'batch_size': batch_size}
accuracy = model_eval(sess, x, y, preds, x_test, y_test, args=eval_params)
assert x_test.shape[0] == test_end - test_start, x_test.shape
print('Test accuracy on legitimate test examples: {0}'.format(accuracy))
report.clean_train_clean_eval = accuracy
###########################################################################
# Craft adversarial examples using Carlini and Wagner's approach
###########################################################################
nb_adv_per_sample = str(nb_classes - 1) if targeted else '1'
print('Crafting ' + str(source_samples) + ' * ' + nb_adv_per_sample +
' adversarial examples')
print("This could take some time ...")
# Instantiate a FGSM attack object
fgsm = FastGradientMethod(model, sess=sess)
if viz_enabled:
assert source_samples == nb_classes
idxs = [np.where(np.argmax(y_test, axis=1) == i)[0][0]
for i in range(nb_classes)]
if targeted:
if viz_enabled:
# Initialize our array for grid visualization
grid_shape = (nb_classes, 1, img_rows, img_cols,
nchannels)
grid_viz_data = np.zeros(grid_shape, dtype='f')
adv_inputs = np.array(
[[instance] * nb_classes for instance in x_test[idxs]],
dtype=np.float32)
else:
adv_inputs = np.array(
[[instance] * nb_classes for
instance in x_test[:source_samples]], dtype=np.float32)
one_hot = np.zeros((nb_classes, nb_classes))
one_hot[np.arange(nb_classes), np.arange(nb_classes)] = 1
adv_inputs = adv_inputs.reshape(
(source_samples * nb_classes, img_rows, img_cols, nchannels))
adv_ys = np.array([one_hot] * source_samples,
dtype=np.float32).reshape((source_samples *
nb_classes, nb_classes))
yname = "y_target"
else:
if viz_enabled:
# Initialize our array for grid visualization
grid_shape = (nb_classes, nb_classes, img_rows, img_cols, nchannels)
grid_viz_data = np.zeros(grid_shape, dtype='f')
adv_inputs = x_test[idxs]
else:
adv_inputs = x_test[:source_samples]
adv_ys = None
yname = "y"
if targeted:
fgsm_params_batch_size = source_samples * nb_classes
else:
fgsm_params_batch_size = source_samples
fgsm_params = {'eps': 0.3,
'clip_min': 0.,
'clip_max': 1.}
adv = fgsm.generate_np(adv_inputs,
**fgsm_params)
eval_params = {'batch_size': np.minimum(nb_classes, source_samples)}
if targeted:
adv_accuracy = model_eval(
sess, x, y, preds, adv, adv_ys, args=eval_params)
else:
if viz_enabled:
err = model_eval(sess, x, y, preds, adv, y_test[idxs], args=eval_params)
adv_accuracy = 1 - err
else:
err = model_eval(sess, x, y, preds, adv, y_test[:source_samples],
args=eval_params)
adv_accuracy = 1 - err
if viz_enabled:
for i in range(nb_classes):
if noise_output:
image = adv[i * nb_classes] - adv_inputs[i * nb_classes]
else:
image = adv[i * nb_classes]
grid_viz_data[i, 0] = image
print('--------------------------------------')
# Compute the number of adversarial examples that were successfully found
print('Avg. rate of successful adv. examples {0:.4f}'.format(adv_accuracy))
report.clean_train_adv_eval = 1. - adv_accuracy
# Compute the average distortion introduced by the algorithm
percent_perturbed = np.mean(np.sum((adv - adv_inputs)**2,
axis=(1, 2, 3))**.5)
print('Avg. L_2 norm of perturbations {0:.4f}'.format(percent_perturbed))
###########################################################################
# Adversarial Training
###########################################################################
model2 = ModelBasicCNN('model2', nb_classes, nb_filters)
fgsm2 = FastGradientMethod(model2, sess=sess)
def attack_fgsm(x):
return fgsm2.generate(adv_inputs, **fgsm_params)
preds2 = model2.get_logits(x)
loss2 = CrossEntropy(model2, smoothing=0.1, attack=attack_fgsm)
train(sess, loss2, x_train, y_train, args=train_params, rng=rng)
eval_params = {'batch_size': batch_size}
accuracy = model_eval(sess, x, y, preds2, x_test, y_test, args=eval_params)
assert x_test.shape[0] == test_end - test_start, x_test.shape
print('Test accuracy on adversarial fgsm test examples: {0}'.format(accuracy))
report.clean_train_clean_eval = accuracy
print("Defined TensorFlow model graph.")
eval_params = {'batch_size': np.minimum(nb_classes, source_samples)}
if targeted:
adv_accuracy = model_eval(
sess, x, y, preds, adv, adv_ys, args=eval_params)
else:
if viz_enabled:
err = model_eval(sess, x, y, preds, adv, y_test[idxs], args=eval_params)
adv_accuracy = 1 - err
else:
err = model_eval(sess, x, y, preds, adv, y_test[:source_samples],
args=eval_params)
adv_accuracy = 1 - err
if viz_enabled:
for i in range(nb_classes):
if noise_output:
image = adv[i * nb_classes] - adv_inputs[i * nb_classes]
else:
image = adv[i * nb_classes]
grid_viz_data[i, 0] = image
print('--------------------------------------')
# Compute the number of adversarial examples that were successfully found
print('Avg. rate of successful adv. examples {0:.4f}'.format(adv_accuracy))
report.clean_train_adv_eval = 1. - adv_accuracy
# Compute the average distortion introduced by the algorithm
percent_perturbed = np.mean(np.sum((adv - adv_inputs)**2,
axis=(1, 2, 3))**.5)
print('Avg. L_2 norm of perturbations {0:.4f}'.format(percent_perturbed))
# Close TF session
sess.close()
def save_visual(data, path):
"""
Modified version of cleverhans.plot.pyplot
"""
figure = plt.figure()
# figure.canvas.set_window_title('Cleverhans: Grid Visualization')
# Add the images to the plot
num_cols = data.shape[0]
num_rows = data.shape[1]
num_channels = data.shape[4]
for y in range(num_rows):
for x in range(num_cols):
figure.add_subplot(num_rows, num_cols, (x + 1) + (y * num_cols))
plt.axis('off')
if num_channels == 1:
plt.imshow(data[x, y, :, :, 0], cmap='gray')
else:
plt.imshow(data[x, y, :, :, :])
# Draw the plot and return
plt.savefig(path)
return figure
# Finally, block & display a grid of all the adversarial examples
if viz_enabled:
# _ = grid_visual(grid_viz_data)
# cleverhans_image.save("output", grid_viz_data)
if noise_output:
image_name = "output/fgsm_mnist_noise.png"
else:
image_name = "output/fgsm_mnist.png"
_ = save_visual(grid_viz_data, image_name)
return report
def main(argv=None):
model_path = FLAGS.model_path
targeted = True if FLAGS.targeted else False
scale = True if FLAGS.scale else False
learning_rate = FLAGS.learning_rate
nb_filters = FLAGS.nb_filters
batch_size = FLAGS.batch_size
nb_epochs = FLAGS.nb_epochs
delay = FLAGS.delay
eps = FLAGS.eps
adv = FLAGS.adv
attack = FLAGS.attack
attack_iterations = FLAGS.attack_iterations
nb_iter = FLAGS.nb_iter
#### EMPIR extra flags
lowprecision=FLAGS.lowprecision
abits=FLAGS.abits
wbits=FLAGS.wbits
abitsList=FLAGS.abitsList
wbitsList=FLAGS.wbitsList
stocRound=True if FLAGS.stocRound else False
rand=FLAGS.rand
model_path2 = FLAGS.model_path2
model_path1 = FLAGS.model_path1
model_path3 = FLAGS.model_path3
ensembleThree=True if FLAGS.ensembleThree else False
abits2=FLAGS.abits2
wbits2=FLAGS.wbits2
abits2List=FLAGS.abits2List
wbits2List=FLAGS.wbits2List
####
save = False
train_from_scratch = False
#### Imagenet flags
imagenet_path = FLAGS.imagenet_path
if imagenet_path is None:
print("Error: Imagenet data path not specified")
sys.exit(1)
# Imagenet specific dimensions
img_rows = _DEFAULT_IMAGE_SIZE
img_cols = _DEFAULT_IMAGE_SIZE
channels = _NUM_CHANNELS
nb_classes = _NUM_CLASSES
# Set TF random seed to improve reproducibility
tf.set_random_seed(1234)
if not hasattr(backend, "tf"):
raise RuntimeError("This tutorial requires keras to be configured"
" to use the TensorFlow backend.")
# Image dimensions ordering should follow the Theano convention
if keras.backend.image_dim_ordering() != 'tf':
keras.backend.set_image_dim_ordering('tf')
print("INFO: '~/.keras/keras.json' sets 'image_dim_ordering' to "
"'th', temporarily setting to 'tf'")
# Create TF session and set as Keras backend session
sess = tf.Session()
keras.backend.set_session(sess)
set_log_level(logging.WARNING)
# Get imagenet datasets
train_dataset, test_dataset = data_imagenet(nb_epochs, batch_size, imagenet_path)
# Creating a initializable iterators
train_iterator = train_dataset.make_initializable_iterator()
test_iterator = test_dataset.make_initializable_iterator()
# Getting next elements from the iterators
next_test_element = test_iterator.get_next()
next_train_element = train_iterator.get_next()
train_x, train_y = train_iterator.get_next()
test_x, test_y = test_iterator.get_next()
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, channels))
y = tf.placeholder(tf.float32, shape=(None, nb_classes))
phase = tf.placeholder(tf.bool, name="phase")
logits_scalar = tf.placeholder_with_default(
INIT_T, shape=(), name="logits_temperature")
if ensembleThree:
if (model_path1 is None or model_path2 is None or model_path3 is None):
train_from_scratch = True
else:
train_from_scratch = False
elif model_path is not None:
if os.path.exists(model_path):
# check for existing model in immediate subfolder
if any(f.endswith('.meta') for f in os.listdir(model_path)):
train_from_scratch = False
else:
model_path = build_model_save_path(
model_path, batch_size, nb_filters, learning_rate, nb_epochs, adv, delay)
print(model_path)
save = True
train_from_scratch = True
else:
train_from_scratch = True # train from scratch, but don't save since no path given
if ensembleThree:
if (wbitsList is None) or (abitsList is None): # Layer wise separate quantization not specified for first model
if (wbits==0) or (abits==0):
print("Error: the number of bits for constant precision weights and activations across layers for the first model have to specified using wbits1 and abits1 flags")
sys.exit(1)
else:
fixedPrec1 = 1
elif (len(wbitsList) != 6) or (len(abitsList) != 6):
print("Error: Need to specify the precisions for activations and weights for the atleast the four convolutional layers of alexnet excluding the first layer and 2 fully connected layers excluding the last layer of the first model")
sys.exit(1)
else:
fixedPrec1 = 0
if (wbits2List is None) or (abits2List is None): # Layer wise separate quantization not specified for second model
if (wbits2==0) or (abits2==0):
print("Error: the number of bits for constant precision weights and activations across layers for the second model have to specified using wbits1 and abits1 flags")
sys.exit(1)
else:
fixedPrec2 = 1
elif (len(wbits2List) != 6) or (len(abits2List) != 6):
print("Error: Need to specify the precisions for activations and weights for the atleast the four convolutional layers of alexnet excluding the first layer and 2 fully connected layers excluding the last layer of the second model")
sys.exit(1)
else:
fixedPrec2 = 0
if (fixedPrec2 != 1) or (fixedPrec1 != 1): # Atleast one of the models have separate precisions per layer
fixedPrec=0
print("Within atleast one model has separate precisions")
if (fixedPrec1 == 1): # first layer has fixed precision
abitsList = (abits, abits, abits, abits, abits, abits)
wbitsList = (wbits, wbits, wbits, wbits, wbits, wbits)
if (fixedPrec2 == 1): # second layer has fixed precision
abits2List = (abits2, abits2, abits2, abits2, abits2, abits2)
wbits2List = (wbits2, wbits2, wbits2, wbits2, wbits2, wbits2)
else:
fixedPrec=1
if (train_from_scratch):
print ("The ensemble model cannot be trained from scratch")
sys.exit(1)
if fixedPrec == 1:
from cleverhans_tutorials.tutorial_models import make_ensemble_three_alexnet
model = make_ensemble_three_alexnet(
phase, logits_scalar, 'lp1_', 'lp2_', 'fp_', wbits, abits, wbits2, abits2, input_shape=(None, img_rows, img_cols, channels), nb_filters=nb_filters, nb_classes=nb_classes)
else:
from cleverhans_tutorials.tutorial_models import make_layerwise_three_combined_alexnet
model = make_layerwise_three_combined_alexnet(
phase, logits_scalar, 'lp1_', 'lp2_', 'fp_', wbitsList, abitsList, wbits2List, abits2List, input_shape=(None, img_rows, img_cols, channels), nb_filters=nb_filters, nb_classes=nb_classes)
elif lowprecision:
if (wbitsList is None) or (abitsList is None): # Layer wise separate quantization not specified
if (wbits==0) or (abits==0):
print("Error: the number of bits for constant precision weights and activations across layers have to specified using wbits and abits flags")
sys.exit(1)
else:
fixedPrec = 1
elif (len(wbitsList) != 6) or (len(abitsList) != 6):
print("Error: Need to specify the precisions for activations and weights for the atleast the four convolutional layers of alexnet excluding the first layer and 2 fully connected layers excluding the last layer")
sys.exit(1)
else:
fixedPrec = 0
if fixedPrec:
### For training from scratch
from cleverhans_tutorials.tutorial_models import make_basic_lowprecision_alexnet
model = make_basic_lowprecision_alexnet(phase, logits_scalar, 'lp_', wbits, abits, input_shape=(
None, img_rows, img_cols, channels), nb_filters=nb_filters, nb_classes=nb_classes)
else:
from cleverhans_tutorials.tutorial_models import make_layerwise_lowprecision_alexnet
model = make_layerwise_lowprecision_alexnet(phase, logits_scalar, 'lp_', wbitsList, abitsList,
input_shape=(None, img_rows, img_cols, channels), nb_filters=nb_filters, nb_classes=nb_classes)
else:
### For training from scratch
from cleverhans_tutorials.tutorial_models import make_basic_alexnet_from_scratch
model = make_basic_alexnet_from_scratch(phase, logits_scalar, 'fp_', input_shape=(
None, img_rows, img_cols, channels), nb_filters=nb_filters, nb_classes=nb_classes)
# separate calling function for ensemble models
if ensembleThree:
preds = model.ensemble_call(x, reuse=False)
else:
##default
preds = model(x, reuse=False)
print("Defined TensorFlow model graph.")
rng = np.random.RandomState([2017, 8, 30])
def evaluate():
# Evaluate the accuracy of the CIFAR10 model on legitimate test
# examples
eval_params = {'batch_size': batch_size}
if ensembleThree:
acc = model_eval_ensemble_imagenet(
sess, x, y, preds, test_iterator, test_x, test_y, phase=phase, args=eval_params)
else: #default below
acc = model_eval_imagenet(
sess, x, y, preds, test_iterator, test_x, test_y, phase=phase, args=eval_params)
print('Test accuracy on legitimate examples: %0.4f' % acc)
# Train an Imagenet model
train_params = {
'lowprecision': lowprecision,
'nb_epochs': nb_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate,
'loss_name': 'train loss',
'filename': 'model',
'reuse_global_step': False,
'train_scope': 'train',
'is_training': True
}
if adv != 0:
if adv == ADVERSARIAL_TRAINING_MADRYETAL:
from cleverhans.attacks import MadryEtAl
train_attack_params = {'eps': MAX_EPS, 'eps_iter': 0.01,
'nb_iter': nb_iter}
train_attacker = MadryEtAl(model, sess=sess)
elif adv == ADVERSARIAL_TRAINING_FGSM:
from cleverhans.attacks import FastGradientMethod
stddev = int(np.ceil((MAX_EPS * 255) // 2))
train_attack_params = {'eps': tf.abs(tf.truncated_normal(
shape=(batch_size, 1, 1, 1), mean=0, stddev=stddev))}
train_attacker = FastGradientMethod(model, back='tf', sess=sess)
# create the adversarial trainer
train_attack_params.update({'clip_min': 0., 'clip_max': 1.})
adv_x_train = train_attacker.generate(x, phase, **train_attack_params)
preds_adv_train = model.get_probs(adv_x_train)
eval_attack_params = {'eps': MAX_EPS, 'clip_min': 0., 'clip_max': 1.}
adv_x_eval = train_attacker.generate(x, phase, **eval_attack_params)
preds_adv_eval = model.get_probs(adv_x_eval) # * logits_scalar
# if adv:
# from cleverhans.attacks import FastGradientMethod
# fgsm = FastGradientMethod(model, back='tf', sess=sess)
# fgsm_params = {'eps': eps, 'clip_min': 0., 'clip_max': 1.}
# adv_x_train = fgsm.generate(x, phase, **fgsm_params)
# preds_adv = model.get_probs(adv_x_train)
if train_from_scratch:
if save:
train_params.update({'log_dir': model_path})
if adv and delay > 0:
train_params.update({'nb_epochs': delay})
# do clean training for 'nb_epochs' or 'delay' epochs with learning rate reducing with time
model_train_imagenet2(sess, x, y, preds, train_iterator, train_x, train_y, phase=phase,
evaluate=evaluate, args=train_params, save=save, rng=rng)
# optionally do additional adversarial training
if adv:
print("Adversarial training for %d epochs" % (nb_epochs - delay))
train_params.update({'nb_epochs': nb_epochs - delay})
train_params.update({'reuse_global_step': True})
model_train_imagenet(sess, x, y, preds, train_iterator, train_x, train_y, phase=phase,
predictions_adv=preds_adv_train, evaluate=evaluate, args=train_params, save=save, rng=rng)
else:
if ensembleThree: ## ensembleThree models have to loaded from different paths
variables = tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES)
# First 11 variables from path1
stored_variables = ['lp_conv1_init/k', 'lp_conv1_init/b', 'lp_conv2_init/k', 'lp_conv3_init/k', 'lp_conv4_init/k', 'lp_conv5_init/k', 'lp_ip1init/W', 'lp_ip1init/b', 'lp_ip2init/W', 'lp_logits_init/W', 'lp_logits_init/b']
variable_dict = dict(OrderedDict(zip(stored_variables, variables[:11]))) # only dict was messing with the order
# Restore the first set of variables from model_path1
saver = tf.train.Saver(variable_dict)
saver.restore(sess, tf.train.latest_checkpoint(model_path1))
# Restore the second set of variables from model_path2
# Second 11 variables from path2
variable_dict = dict(OrderedDict(zip(stored_variables, variables[11:22])))
saver2 = tf.train.Saver(variable_dict)
saver2.restore(sess, tf.train.latest_checkpoint(model_path2))
# Third 11 variables from path3
stored_variables = ['fp_conv1_init/k', 'fp_conv1_init/b', 'fp_conv2_init/k', 'fp_conv3_init/k', 'fp_conv4_init/k', 'fp_conv5_init/k', 'fp_ip1init/W', 'fp_ip1init/b', 'fp_ip2init/W', 'fp_logits_init/W', 'fp_logits_init/b']
variable_dict = dict(OrderedDict(zip(stored_variables, variables[22:33])))
saver3 = tf.train.Saver(variable_dict)
saver3.restore(sess, tf.train.latest_checkpoint(model_path3))
# Next 24 batch norm variables from path1
stored_variables = ['lp__batchNorm1/batch_normalization/gamma', 'lp__batchNorm1/batch_normalization/beta', 'lp__batchNorm1/batch_normalization/moving_mean', 'lp__batchNorm1/batch_normalization/moving_variance', 'lp__batchNorm2/batch_normalization/gamma', 'lp__batchNorm2/batch_normalization/beta', 'lp__batchNorm2/batch_normalization/moving_mean', 'lp__batchNorm2/batch_normalization/moving_variance', 'lp__batchNorm3/batch_normalization/gamma', 'lp__batchNorm3/batch_normalization/beta', 'lp__batchNorm3/batch_normalization/moving_mean', 'lp__batchNorm3/batch_normalization/moving_variance', 'lp__batchNorm4/batch_normalization/gamma', 'lp__batchNorm4/batch_normalization/beta', 'lp__batchNorm4/batch_normalization/moving_mean', 'lp__batchNorm4/batch_normalization/moving_variance', 'lp__batchNorm5/batch_normalization/gamma', 'lp__batchNorm5/batch_normalization/beta', 'lp__batchNorm5/batch_normalization/moving_mean', 'lp__batchNorm5/batch_normalization/moving_variance', 'lp__batchNorm6/batch_normalization/gamma', 'lp__batchNorm6/batch_normalization/beta', 'lp__batchNorm6/batch_normalization/moving_mean', 'lp__batchNorm6/batch_normalization/moving_variance']
variable_dict = dict(OrderedDict(zip(stored_variables, variables[33:57])))
saver = tf.train.Saver(variable_dict)
saver.restore(sess, tf.train.latest_checkpoint(model_path1))
# Next 24 batch norm variables from path2
variable_dict = dict(OrderedDict(zip(stored_variables, variables[57:81])))
saver = tf.train.Saver(variable_dict)
saver.restore(sess, tf.train.latest_checkpoint(model_path2))
# Final 24 batch norm variables from path1
stored_variables = ['fp__batchNorm1/batch_normalization/gamma', 'fp__batchNorm1/batch_normalization/beta', 'fp__batchNorm1/batch_normalization/moving_mean', 'fp__batchNorm1/batch_normalization/moving_variance', 'fp__batchNorm2/batch_normalization/gamma', 'fp__batchNorm2/batch_normalization/beta', 'fp__batchNorm2/batch_normalization/moving_mean', 'fp__batchNorm2/batch_normalization/moving_variance', 'fp__batchNorm3/batch_normalization/gamma', 'fp__batchNorm3/batch_normalization/beta', 'fp__batchNorm3/batch_normalization/moving_mean', 'fp__batchNorm3/batch_normalization/moving_variance', 'fp__batchNorm4/batch_normalization/gamma', 'fp__batchNorm4/batch_normalization/beta', 'fp__batchNorm4/batch_normalization/moving_mean', 'fp__batchNorm4/batch_normalization/moving_variance', 'fp__batchNorm5/batch_normalization/gamma', 'fp__batchNorm5/batch_normalization/beta', 'fp__batchNorm5/batch_normalization/moving_mean', 'fp__batchNorm5/batch_normalization/moving_variance', 'fp__batchNorm6/batch_normalization/gamma', 'fp__batchNorm6/batch_normalization/beta', 'fp__batchNorm6/batch_normalization/moving_mean', 'fp__batchNorm6/batch_normalization/moving_variance']
variable_dict = dict(OrderedDict(zip(stored_variables, variables[81:105])))
saver = tf.train.Saver(variable_dict)
saver.restore(sess, tf.train.latest_checkpoint(model_path3))
else: # restoring the model trained using this setup, not a downloaded one
tf_model_load(sess, model_path)
print('Restored model from %s' % model_path)
# evaluate()
# Evaluate the accuracy of the model on legitimate test examples
eval_params = {'batch_size': batch_size}
if ensembleThree:
accuracy = model_eval_ensemble_imagenet(sess, x, y, preds, test_iterator, test_x, test_y, phase=phase, feed={phase: False}, args=eval_params)
else: #default below
accuracy = model_eval_imagenet(sess, x, y, preds, test_iterator, test_x, test_y, phase=phase, feed={phase: False}, args=eval_params)
print('Test accuracy on legitimate test examples: {0}'.format(accuracy))
###########################################################################
# Build dataset
###########################################################################
adv_inputs = test_x #adversarial inputs can be generated from any of the test examples
###########################################################################
# Craft adversarial examples using generic approach
###########################################################################
nb_adv_per_sample = 1
adv_ys = None
yname = "y"
print('Crafting adversarial examples')
print("This could take some time ...")
if ensembleThree:
model_type = 'ensembleThree'
else:
model_type = 'default'
if attack == ATTACK_CARLINI_WAGNER_L2:
from cleverhans.attacks import CarliniWagnerL2
attacker = CarliniWagnerL2(model, back='tf', sess=sess, model_type=model_type, num_classes=nb_classes)
attack_params = {'binary_search_steps': 1,
'max_iterations': attack_iterations,
'learning_rate': 0.1,
'batch_size': batch_size,
'initial_const': 10,
}
elif attack == ATTACK_JSMA:
from cleverhans.attacks import SaliencyMapMethod
attacker = SaliencyMapMethod(model, back='tf', sess=sess, model_type=model_type, num_classes=nb_classes)
attack_params = {'theta': 1., 'gamma': 0.1}
elif attack == ATTACK_FGSM:
from cleverhans.attacks import FastGradientMethod
attacker = FastGradientMethod(model, back='tf', sess=sess, model_type=model_type, num_classes=nb_classes)
attack_params = {'eps': eps}
elif attack == ATTACK_MADRYETAL:
from cleverhans.attacks import MadryEtAl
attacker = MadryEtAl(model, back='tf', sess=sess, model_type=model_type, num_classes=nb_classes)
attack_params = {'eps': eps, 'eps_iter': 0.01, 'nb_iter': nb_iter}
elif attack == ATTACK_BASICITER:
print('Attack: BasicIterativeMethod')
from cleverhans.attacks import BasicIterativeMethod
attacker = BasicIterativeMethod(model, back='tf', sess=sess, model_type=model_type, num_classes=nb_classes)
attack_params = {'eps': eps, 'eps_iter': 0.01, 'nb_iter': nb_iter}
else:
print("Attack undefined")
sys.exit(1)
attack_params.update({'clip_min': -2.2, 'clip_max': 2.7}) # Since max and min for imagenet turns out to be around -2.11 and 2.12
eval_params = {'batch_size': batch_size}
'''
adv_x = attacker.generate(x, phase, **attack_params)
# Craft adversarial examples using Fast Gradient Sign Method (FGSM)
eval_params = {'batch_size': batch_size}
X_test_adv, = batch_eval(sess, [x], [adv_x], [adv_inputs], feed={
phase: False}, args=eval_params)
'''
print("Evaluating un-targeted results")
if ensembleThree:
adv_accuracy = model_eval_ensemble_adv_imagenet(sess, x, y, preds, test_iterator,
test_x, test_y, phase=phase, args=eval_params, attacker=attacker, attack_params=attack_params)
else:
adv_accuracy = model_eval_adv_imagenet(sess, x, y, preds, test_iterator,
test_x, test_y, phase=phase, args=eval_params, attacker=attacker, attack_params=attack_params)
# Compute the number of adversarial examples that were successfully found
print('Test accuracy on adversarial examples {0:.4f}'.format(adv_accuracy))
# Close TF session
sess.close()
X_train,
Y_train,
evaluate=evaluate,
args=train_params,
rng=rng,
save=True)
eval_par = {'batch_size': batch_size}
acc = model_eval(sess, x, y, preds, X_train, Y_train, args=eval_par)
print('Train accuracy on legitimate examples: %0.4f\n' % acc)
acc = model_eval(sess, x, y, preds, X_test, Y_test, args=eval_par)
print('Test accuracy on legitimate examples: %0.4f\n' % acc)
if not TRAIN:
tf_model_load(sess, cache_dir + "/" + MODEL_NAME)
fgsm_params = {'eps': 0.25, 'clip_min': 0., 'clip_max': 1.}
fgsm = FastGradientMethod(model, sess=sess)
adversarial_sample = fgsm.generate(x, **fgsm_params)
# Predictions and class of the adversarial examples.
adversarial_preds = model.get_probs(adversarial_sample)
adversarial_class = tf.argmax(adversarial_preds, axis=-1)
X_adv = np.ndarray(shape=X_test.shape)
Y_adv = np.copy(Y_test)
X_slic = np.ndarray(shape=X_test.shape)
Y_slic = np.copy(Y_test)
def prep_bbox(sess,
logits_scalar,
x,
y,
X_train,
Y_train,
X_test,
Y_test,
img_rows,
img_cols,
channels,
nb_epochs,
batch_size,
learning_rate,
rng,
phase=None,
binary=False,
scale=False,
nb_filters=64,
model_path=None,
adv=0,
delay=0,
eps=0.3):
"""
Define and train a model that simulates the "remote"
black-box oracle described in the original paper.
:param sess: the TF session
:param x: the input placeholder for MNIST
:param y: the ouput placeholder for MNIST
:param X_train: the training data for the oracle
:param Y_train: the training labels for the oracle
:param X_test: the testing data for the oracle
:param Y_test: the testing labels for the oracle
:param nb_epochs: number of epochs to train model
:param batch_size: size of training batches
:param learning_rate: learning rate for training
:param rng: numpy.random.RandomState
:return:
"""
# Define TF model graph (for the black-box model)
save = False
train_from_scratch = False
if model_path is not None:
if os.path.exists(model_path):
# check for existing model in immediate subfolder
if any(f.endswith('.meta') for f in os.listdir(model_path)):
binary, scale, nb_filters, batch_size, learning_rate, nb_epochs, adv = parse_model_settings(
model_path)
train_from_scratch = False
else:
model_path = build_model_save_path(model_path, binary,
batch_size, nb_filters,
learning_rate, nb_epochs,
adv, delay, scale)
print(model_path)
save = True
train_from_scratch = True
else:
train_from_scratch = True # train from scratch, but don't save since no path given
if binary:
if scale:
#from cleverhans_tutorials.tutorial_models import make_scaled_binary_cnn
# model = make_scaled_binary_cnn(phase, 'bb_binsc_', input_shape=(
from cleverhans_tutorials.tutorial_models import make_scaled_binary_rand_cnn
model = make_scaled_binary_rand_cnn(phase,
logits_scalar,
'bb_binsc_',
input_shape=(None, img_rows,
img_cols,
channels),
nb_filters=nb_filters)
else:
from cleverhans_tutorials.tutorial_models import make_basic_binary_cnn
model = make_basic_binary_cnn(phase,
logits_scalar,
'bb_bin_',
input_shape=(None, img_rows,
img_cols, channels),
nb_filters=nb_filters)
else:
from cleverhans_tutorials.tutorial_models import make_basic_cnn
model = make_basic_cnn(phase,
logits_scalar,
'bb_fp_',
input_shape=(None, img_rows, img_cols,
channels),
nb_filters=nb_filters)
preds = model(x, reuse=False)
print("Defined TensorFlow model graph.")
def evaluate():
# Print out the accuracy on legitimate data
eval_params = {'batch_size': batch_size}
acc = model_eval(sess,
x,
y,
preds,
X_test,
Y_test,
phase=phase,
args=eval_params)
print('Test accuracy of black-box on legitimate test '
'examples: %.4f' % acc)
# Train an MNIST model
train_params = {
'binary': binary,
'nb_epochs': nb_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate,
'loss_name': 'bb train loss',
'filename': 'bb_model',
'train_scope': 'bb_model',
'reuse_global_step': False,
'is_training': True
}
if adv != 0:
if adv == ADVERSARIAL_TRAINING_MADRYETAL:
from cleverhans.attacks import MadryEtAl
nb_iter = 20
train_attack_params = {
'eps': MAX_EPS,
'eps_iter': 0.01,
'nb_iter': nb_iter
}
train_attacker = MadryEtAl(model, sess=sess)
if adv == ADVERSARIAL_TRAINING_FGSM:
from cleverhans.attacks import FastGradientMethod
train_attacker = FastGradientMethod(model, back='tf', sess=sess)
# create the adversarial trainer
train_attack_params.update({'clip_min': 0., 'clip_max': 1.})
adv_x_train = train_attacker.generate(x, phase, **train_attack_params)
preds_adv = model.get_probs(adv_x_train)
if train_from_scratch:
if save:
train_params.update({'log_dir': model_path})
if adv and delay > 0:
train_params.update({'nb_epochs': delay})
# do clean training for 'nb_epochs' or 'delay' epochs
model_train(sess,
x,
y,
preds,
X_train,
Y_train,
phase=phase,
evaluate=evaluate,
args=train_params,
save=save,
rng=rng)
# optionally do additional adversarial training
if adv:
print("Adversarial training for %d epochs" % (nb_epochs - delay))
train_params.update({'nb_epochs': nb_epochs - delay})
train_params.update({'reuse_global_step': True})
model_train(sess,
x,
y,
preds,
X_train,
Y_train,
phase=phase,
predictions_adv=preds_adv,
evaluate=evaluate,
args=train_params,
save=save,
rng=rng)
else:
tf_model_load(sess, model_path)
print('Restored model from %s' % model_path)
accuracy = evaluate()
return model, preds, accuracy, model_path
def mnist_tutorial_cw(train_start=0,
train_end=60000,
test_start=0,
test_end=10000,
viz_enabled=True,
nb_epochs=6,
batch_size=128,
source_samples=10,
learning_rate=0.001,
attack_iterations=100,
model_path=os.path.join("models", "mnist"),
targeted=True):
"""
MNIST tutorial for Carlini and Wagner's attack
:param train_start: index of first training set example
:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
:param viz_enabled: (boolean) activate plots of adversarial examples
:param nb_epochs: number of epochs to train model
:param batch_size: size of training batches
:param nb_classes: number of output classes
:param source_samples: number of test inputs to attack
:param learning_rate: learning rate for training
:param model_path: path to the model file
:param targeted: should we run a targeted attack? or untargeted?
:return: an AccuracyReport object
"""
# Object used to keep track of (and return) key accuracies
report = AccuracyReport()
# Set TF random seed to improve reproducibility
tf.set_random_seed(1234)
# Create TF session
sess = tf.Session()
print("Created TensorFlow session.")
set_log_level(logging.DEBUG)
# Get MNIST test data
x_train, y_train, x_test, y_test = data_mnist(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end)
# Obtain Image Parameters
img_rows, img_cols, nchannels = x_train.shape[1:4]
nb_classes = y_train.shape[1]
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, nchannels))
y = tf.placeholder(tf.float32, shape=(None, nb_classes))
nb_filters = 64
# Define TF model graph
model = ModelBasicCNN('model1', nb_classes, nb_filters)
preds = model.get_logits(x)
loss = LossCrossEntropy(model, smoothing=0.1)
print("Defined TensorFlow model graph.")
###########################################################################
# Training the model using TensorFlow
###########################################################################
# Train an MNIST model
train_params = {
'nb_epochs': nb_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate,
'train_dir': os.path.join(*os.path.split(model_path)[:-1]),
'filename': os.path.split(model_path)[-1]
}
rng = np.random.RandomState([2017, 8, 30])
# check if we've trained before, and if we have, use that pre-trained model
if os.path.exists(model_path + ".meta"):
tf_model_load(sess, model_path)
else:
train(sess,
loss,
x,
y,
x_train,
y_train,
args=train_params,
save=os.path.exists("models"),
rng=rng)
# Evaluate the accuracy of the MNIST model on legitimate test examples
eval_params = {'batch_size': batch_size}
accuracy = model_eval(sess, x, y, preds, x_test, y_test, args=eval_params)
assert x_test.shape[0] == test_end - test_start, x_test.shape
print('Test accuracy on legitimate test examples: {0}'.format(accuracy))
report.clean_train_clean_eval = accuracy
###########################################################################
# Craft adversarial examples using Carlini and Wagner's approach
###########################################################################
nb_adv_per_sample = str(nb_classes - 1) if targeted else '1'
print('Crafting ' + str(source_samples) + ' * ' + nb_adv_per_sample +
' adversarial examples')
print("This could take some time ...")
# Instantiate a CW attack object
cw = CarliniWagnerL2(model, back='tf', sess=sess)
if viz_enabled:
assert source_samples == nb_classes
idxs = [
np.where(np.argmax(y_test, axis=1) == i)[0][0]
for i in range(nb_classes)
]
if targeted:
if viz_enabled:
# Initialize our array for grid visualization
grid_shape = (nb_classes, nb_classes, img_rows, img_cols,
nchannels)
grid_viz_data = np.zeros(grid_shape, dtype='f')
adv_inputs = np.array([[instance] * nb_classes
for instance in x_test[idxs]],
dtype=np.float32)
else:
adv_inputs = np.array([[instance] * nb_classes
for instance in x_test[:source_samples]],
dtype=np.float32)
one_hot = np.zeros((nb_classes, nb_classes))
one_hot[np.arange(nb_classes), np.arange(nb_classes)] = 1
adv_inputs = adv_inputs.reshape(
(source_samples * nb_classes, img_rows, img_cols, nchannels))
adv_ys = np.array([one_hot] * source_samples,
dtype=np.float32).reshape(
(source_samples * nb_classes, nb_classes))
yname = "y_target"
else:
if viz_enabled:
# Initialize our array for grid visualization
grid_shape = (nb_classes, 2, img_rows, img_cols, nchannels)
grid_viz_data = np.zeros(grid_shape, dtype='f')
adv_inputs = x_test[idxs]
else:
adv_inputs = x_test[:source_samples]
adv_ys = None
yname = "y"
cw_params = {
'binary_search_steps': 1,
yname: adv_ys,
'max_iterations': attack_iterations,
'learning_rate': 0.1,
'batch_size':
source_samples * nb_classes if targeted else source_samples,
'initial_const': 10
}
adv = cw.generate_np(adv_inputs, **cw_params)
eval_params = {'batch_size': np.minimum(nb_classes, source_samples)}
if targeted:
adv_accuracy = model_eval(sess,
x,
y,
preds,
adv,
adv_ys,
args=eval_params)
else:
if viz_enabled:
adv_accuracy = 1 - \
model_eval(sess, x, y, preds, adv, y_test[
idxs], args=eval_params)
else:
adv_accuracy = 1 - \
model_eval(sess, x, y, preds, adv, y_test[
:source_samples], args=eval_params)
if viz_enabled:
for j in range(nb_classes):
if targeted:
for i in range(nb_classes):
grid_viz_data[i, j] = adv[i * nb_classes + j]
else:
grid_viz_data[j, 0] = adv_inputs[j]
grid_viz_data[j, 1] = adv[j]
print(grid_viz_data.shape)
print('--------------------------------------')
# Compute the number of adversarial examples that were successfully found
print('Avg. rate of successful adv. examples {0:.4f}'.format(adv_accuracy))
report.clean_train_adv_eval = 1. - adv_accuracy
# Compute the average distortion introduced by the algorithm
percent_perturbed = np.mean(
np.sum((adv - adv_inputs)**2, axis=(1, 2, 3))**.5)
print('Avg. L_2 norm of perturbations {0:.4f}'.format(percent_perturbed))
# Close TF session
sess.close()
# Finally, block & display a grid of all the adversarial examples
if viz_enabled:
import matplotlib.pyplot as plt
_ = grid_visual(grid_viz_data)
return report
def mnist_tutorial_cw(train_start=0, train_end=60000, test_start=0,
test_end=10000, viz_enabled=True, nb_epochs=6,
batch_size=128, nb_classes=10, source_samples=10,
learning_rate=0.001, attack_iterations=100,
model_path=os.path.join("models", "mnist"),
targeted=True):
"""
MNIST tutorial for Carlini and Wagner's attack
:param train_start: index of first training set example
:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
:param viz_enabled: (boolean) activate plots of adversarial examples
:param nb_epochs: number of epochs to train model
:param batch_size: size of training batches
:param nb_classes: number of output classes
:param source_samples: number of test inputs to attack
:param learning_rate: learning rate for training
:param model_path: path to the model file
:param targeted: should we run a targeted attack? or untargeted?
:return: an AccuracyReport object
"""
# Object used to keep track of (and return) key accuracies
report = AccuracyReport()
# MNIST-specific dimensions
img_rows = 28
img_cols = 28
channels = 1
# Disable Keras learning phase since we will be serving through tensorflow
keras.layers.core.K.set_learning_phase(0)
# Set TF random seed to improve reproducibility
tf.set_random_seed(1234)
# Image dimensions ordering should follow the TensorFlow convention
if keras.backend.image_dim_ordering() != 'tf':
keras.backend.set_image_dim_ordering('tf')
print("INFO: '~/.keras/keras.json' sets 'image_dim_ordering' "
"to 'th', temporarily setting to 'tf'")
# Create TF session and set as Keras backend session
sess = tf.Session()
keras.backend.set_session(sess)
print("Created TensorFlow session and set Keras backend.")
# Get MNIST test data
X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end)
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
y = tf.placeholder(tf.float32, shape=(None, 10))
# Define TF model graph
model = cnn_model()
preds = model(x)
print("Defined TensorFlow model graph.")
###########################################################################
# Training the model using TensorFlow
###########################################################################
# Train an MNIST model
train_params = {
'nb_epochs': nb_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate,
'train_dir': os.path.join(*os.path.split(model_path)[:-1]),
'filename': os.path.split(model_path)[-1]
}
# check if we've trained before, and if we have, use that pre-trained model
if os.path.exists(model_path+".meta"):
tf_model_load(sess, model_path)
else:
model_train(sess, x, y, preds, X_train, Y_train, args=train_params,
save=os.path.exists("models"))
# Evaluate the accuracy of the MNIST model on legitimate test examples
eval_params = {'batch_size': batch_size}
accuracy = model_eval(sess, x, y, preds, X_test, Y_test, args=eval_params)
assert X_test.shape[0] == test_end - test_start, X_test.shape
print('Test accuracy on legitimate test examples: {0}'.format(accuracy))
report.clean_train_clean_eval = accuracy
###########################################################################
# Craft adversarial examples using Carlini and Wagner's approach
###########################################################################
print('Crafting ' + str(source_samples) + ' * ' + str(nb_classes-1) +
' adversarial examples')
print("This could take some time ...")
# Instantiate a CW attack object
wrap = KerasModelWrapper(model)
cw = CarliniWagnerL2(wrap, back='tf', sess=sess)
idxs = [np.where(np.argmax(Y_test, axis=1) == i)[0][0] for i in range(10)]
if targeted:
# Initialize our array for grid visualization
grid_shape = (nb_classes, nb_classes, img_rows, img_cols, channels)
grid_viz_data = np.zeros(grid_shape, dtype='f')
one_hot = np.zeros((10, 10))
one_hot[np.arange(10), np.arange(10)] = 1
adv_inputs = np.array([[instance] * 10 for instance in X_test[idxs]],
dtype=np.float32)
adv_inputs = adv_inputs.reshape((100, 28, 28, 1))
adv_ys = np.array([one_hot] * 10, dtype=np.float32).reshape((100, 10))
yname = "y_target"
else:
# Initialize our array for grid visualization
grid_shape = (nb_classes, 2, img_rows, img_cols, channels)
grid_viz_data = np.zeros(grid_shape, dtype='f')
adv_inputs = X_test[idxs]
adv_ys = None
yname = "y"
cw_params = {'binary_search_steps': 1,
yname: adv_ys,
'max_iterations': attack_iterations,
'learning_rate': 0.1,
'batch_size': 100 if targeted else 10,
'initial_const': 10}
adv = cw.generate_np(adv_inputs,
**cw_params)
if targeted:
adv_accuracy = model_eval(sess, x, y, preds, adv, adv_ys,
args={'batch_size': 10})
else:
adv_accuracy = 1-model_eval(sess, x, y, preds, adv, Y_test[idxs],
args={'batch_size': 10})
for j in range(10):
if targeted:
for i in range(10):
grid_viz_data[i, j] = adv[i * 10 + j]
else:
grid_viz_data[j, 0] = adv_inputs[j]
grid_viz_data[j, 1] = adv[j]
print(grid_viz_data.shape)
print('--------------------------------------')
# Compute the number of adversarial examples that were successfully found
print('Avg. rate of successful adv. examples {0:.4f}'.format(adv_accuracy))
report.clean_train_adv_eval = 1.-adv_accuracy
# Compute the average distortion introduced by the algorithm
percent_perturbed = np.mean(np.sum((adv - adv_inputs)**2,
axis=(1, 2, 3))**.5)
print('Avg. L_2 norm of perturbations {0:.4f}'.format(percent_perturbed))
# Close TF session
sess.close()
# Finally, block & display a grid of all the adversarial examples
if viz_enabled:
import matplotlib.pyplot as plt
_ = grid_visual(grid_viz_data)
return report
def mnist_tutorial(train_start=0,
train_end=60000,
test_start=0,
test_end=10000,
nb_epochs=6,
batch_size=128,
learning_rate=0.001,
clean_train=True,
testing=False,
backprop_through_attack=False,
nb_filters=64):
"""
MNIST cleverhans tutorial
:param train_start: index of first training set example
:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
:param nb_epochs: number of epochs to train model
:param batch_size: size of training batches
:param learning_rate: learning rate for training
:param clean_train: perform normal training on clean examples only
before performing adversarial training.
:param testing: if true, complete an AccuracyReport for unit tests
to verify that performance is adequate
:param backprop_through_attack: If True, backprop through adversarial
example construction process during
adversarial training.
:param clean_train: if true, train on clean examples
:return: an AccuracyReport object
"""
# Object used to keep track of (and return) key accuracies
report = AccuracyReport()
model_path = "./"
model_name = "clean_trained__model_notmnist"
# Set TF random seed to improve reproducibility
tf.set_random_seed(7895)
# Set logging level to see debug information
set_log_level(logging.DEBUG)
# Get MNIST test data
# X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start,
# train_end=train_end,
# test_start=test_start,
# test_end=test_end)
# Get notMNIST data
with np.load("notmnist.npz") as data:
X_train, Y_train, X_test, Y_test = data['examples_train'], data[
'labels_train'], data['examples_test'], data['labels_test']
# Use label smoothing
assert Y_train.shape[1] == 10
label_smooth = .1
Y_train = Y_train.clip(label_smooth / 9., 1. - label_smooth)
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
y = tf.placeholder(tf.float32, shape=(None, 10))
fgsm_params = {'eps': 0.3, 'clip_min': 0., 'clip_max': 1.}
# Define TF model graph
model = make_basic_cnn()
# Create TF session
sess = tf.Session()
if tf_model_load(sess, file_path=os.path.join(model_path, model_name)):
print(model_name, " reloaded.")
# Initialize the Fast Gradient Sign Method (FGSM) attack object and
# graph
fgsm = FastGradientMethod(model, sess=sess)
adv_x = fgsm.generate(x, **fgsm_params)
preds_adv = model.get_probs(adv_x)
# Evaluate the accuracy of the MNIST model on adversarial examples
eval_par = {'batch_size': batch_size}
acc = model_eval(sess, x, y, preds_adv, X_test, Y_test, args=eval_par)
print('Test accuracy on adversarial examples: %0.4f\n' % acc)
report.clean_train_adv_eval = acc
return report
def main(argv=None):
tf.set_random_seed(1234)
sess = tf.Session()
keras.backend.set_session(sess)
X_train, Y_train, X_test, Y_test = data_cifar10()
Y_train = Y_train.clip(.1 / 9., 1. - .1)
x = tf.placeholder(tf.float32, shape=(None, 32, 32, 3))
y = tf.placeholder(tf.float32, shape=(None, 10))
model = cnn_model(img_rows=32, img_cols=32, channels=3)
predictions = model(x)
def evaluate():
eval_params = {'batch_size': FLAGS.batch_size}
accuracy = model_eval(sess,
x,
y,
predictions,
X_test,
Y_test,
args=eval_params)
print('Test accuracy on legitimate test examples: ' + str(accuracy))
train_params = {
'nb_epochs': FLAGS.nb_epochs,
'batch_size': FLAGS.batch_size,
'learning_rate': FLAGS.learning_rate,
'train_dir': FLAGS.train_dir,
'filename': FLAGS.filename
}
model_path = os.path.join(FLAGS.train_dir, FLAGS.filename)
if os.path.exists(model_path + ".meta"):
tf_model_load(sess, model_path)
else:
model_train(sess,
x,
y,
predictions,
X_train,
Y_train,
evaluate=evaluate,
args=train_params,
save=True)
wrap = KerasModelWrapper(model)
nb_classes = 10
targeted = False
nb_adv_per_sample = str(nb_classes - 1) if targeted else '1'
cw = CarliniWagnerL2(model, back='tf', sess=sess)
adv_inputs = X_test[:10]
adv_ys = None
yname = "y"
edges = edgeDetect(adv_inputs)
print('Length of edges of adv samples: ')
print(edges[:10])
cw_params = {
'binary_search_steps': 1,
yname: adv_ys,
'max_iterations': 100,
'learning_rate': 0.1,
'batch_size': 10,
'initial_const': 10,
}
adv = cw.generate_np(adv_inputs, **cw_params)
edges = edgeDetect(adv)
print('Length of edges of adv samples: ')
print(edges[:10])
sigma = 16.0 / 255
gamma = 0.00061 * 255 * 255
alpha = 0.00061 * 255 * 255
n_clusters = 10
n_samples = 50
noise = np.random.normal(0.0, sigma, adv.shape)
adv_gauss = adv + noise
i1 = np.repeat(np.arange(0, 10), n_samples)
i2 = np.random.randint(32, size=10 * n_samples)
i3 = np.random.randint(32, size=10 * n_samples)
sample = adv[i1, i2, i3]
noise = np.random.normal(0.0, sigma, sample.shape)
noisy_samples = sample + noise
noisy_samples = np.reshape(noisy_samples, (10, n_samples, 3))
noise = np.random.normal(0.0, sigma, adv.shape)
adv_rdesc = np.zeros(adv.shape)
adv_rmix = np.zeros(adv.shape)
for img_no, img_samples in enumerate(noisy_samples):
clusters = np.zeros((n_clusters, 3))
clusters[0] = img_samples[0]
for c_j in range(1, n_clusters):
prob_cj = np.zeros(n_samples)
for pix_no, pix in enumerate(img_samples):
l2_min = 100000
for c_l in range(0, c_j):
l2_norm_sq = np.inner(pix - clusters[c_l],
pix - clusters[c_l])
if l2_norm_sq < l2_min:
l2_min = l2_norm_sq
prob_cj[pix_no] = math.exp(gamma * l2_min)
prob_cj /= prob_cj.sum()
clusters[c_j] = img_samples[np.random.choice(n_samples,
1,
p=prob_cj)]
for pix_i in range(0, 32):
for pix_j in range(0, 32):
c_dist_min = 100000
c_min = np.zeros(3)
c_sum = np.zeros(3)
weight_sum = 0
for c_j in clusters:
c_dist = np.linalg.norm(adv_gauss[img_no][pix_i][pix_j] -
c_j)
weight_j = math.exp(-1 * alpha * c_dist * c_dist)
weight_sum = weight_sum + weight_j
c_sum = c_sum + weight_j * c_j
if c_dist < c_dist_min:
c_dist_min = c_dist
c_min = c_j
adv_rdesc[img_no][pix_i][pix_j] = c_min
adv_rmix[img_no][pix_i][pix_j] = c_sum / weight_sum
eval_params = {'batch_size': np.minimum(nb_classes, 10)}
adv_accuracy = 1 - model_eval(
sess, x, y, predictions, adv, Y_test[:10], args=eval_params)
print('Avg. rate of successful adv. examples without noise {0:.4f}'.format(
adv_accuracy))
percent_perturbed = np.mean(
np.sum((adv - adv_inputs)**2, axis=(1, 2, 3))**.5)
print('Avg. L_2 norm of perturbations without noise {0:.4f}'.format(
percent_perturbed))
adv_accuracy = 1 - model_eval(
sess, x, y, predictions, adv_gauss, Y_test[:10], args=eval_params)
print('Avg. rate of successful adv. examples with Gaussian noise {0:.4f}'.
format(adv_accuracy))
percent_perturbed = np.mean(
np.sum((adv_gauss - adv_inputs)**2, axis=(1, 2, 3))**.5)
print('Avg. L_2 norm of perturbations with Gaussian noise {0:.4f}'.format(
percent_perturbed))
adv_accuracy = 1 - model_eval(
sess, x, y, predictions, adv_rdesc, Y_test[:10], args=eval_params)
print('Avg. rate of successful adv. examples with random descent {0:.4f}'.
format(adv_accuracy))
percent_perturbed = np.mean(
np.sum((adv_rdesc - adv_inputs)**2, axis=(1, 2, 3))**.5)
print('Avg. L_2 norm of perturbations with random descent {0:.4f}'.format(
percent_perturbed))
adv_accuracy = 1 - model_eval(
sess, x, y, predictions, adv_rmix, Y_test[:10], args=eval_params)
print('Avg. rate of successful adv. examples with random mixture {0:.4f}'.
format(adv_accuracy))
percent_perturbed = np.mean(
np.sum((adv_rmix - adv_inputs)**2, axis=(1, 2, 3))**.5)
print('Avg. L_2 norm of perturbations with random mixture {0:.4f}'.format(
percent_perturbed))
sess.close()
def main(_):
batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
num_classes = 1001
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
# Prepare graph
x_input = tf.placeholder(tf.float32, shape=batch_shape)
y_label = tf.placeholder(tf.int32, shape=(FLAGS.batch_size, ))
y_hot = tf.one_hot(y_label, num_classes)
model = InceptionModel(num_classes)
preds = model(x_input)
logits = model.get_logits(x_input)
acc = _top_1_accuracy(logits, y_label)
tf_model_load(sess, FLAGS.checkpoint_path)
attack = KKTFun5(model, sess=sess)
eps = FLAGS.eps
alp = FLAGS.alp
params = {
'eps': 0.3,
'alp': 1.0,
'ord': 2,
'nb_iter': eps,
'clip_min': 0.,
'clip_max': 1.
}
adv_x, log_step, log_suc = attack.generate(x_input, y_label, **params)
adv_image = np.zeros((1000, 299, 299, 3))
l2_norm = np.zeros((1000))
acc_ori = np.zeros((1000))
acc_val = np.zeros((1000))
pred_score = np.zeros((1000, 1001))
pred_score_adv = np.zeros((1000, 1001))
name = []
b_i = 0
begin = time.time()
for images, _, labels, filenames in load_images(FLAGS.input_dir,
FLAGS.input_dir,
FLAGS.metadata_file_path,
batch_shape):
bb_i = b_i + FLAGS.batch_size
y_labels = np.zeros((FLAGS.batch_size, num_classes))
for i_y in range(FLAGS.batch_size):
y_labels[i_y][labels[i_y]] = 1
x_adv = sess.run(adv_x, feed_dict={x_input: images, y_label: labels})
#acc_val[b_i:bb_i] = sess.run(log_suc,feed_dict={x_input:images,y_label:labels})
#l2_norm[b_i:bb_i] = sess.run(log_step,feed_dict={x_input:images,y_label:labels})
#pdb.set_trace()
#acc_ori[b_i] = sess.run(acc,feed_dict={x_input:images,y_label:labels})
#l2_norm[b_i] = np.mean(np.sum((images- x_adv)**2,axis=(1,2,3))**.5)
adv_image[b_i:bb_i] = x_adv
acc_ori[b_i:bb_i] = sess.run(acc,
feed_dict={
x_input: images,
y_label: labels
})
acc_val[b_i:bb_i] = sess.run(acc,
feed_dict={
x_input: x_adv,
y_label: labels
})
pred_score[b_i:bb_i] = sess.run(preds,
feed_dict={
x_input: images,
y_label: labels
})
pred_score_adv[b_i:bb_i] = sess.run(preds,
feed_dict={
x_input: x_adv,
y_label: labels
})
l2_norm[b_i:bb_i] = np.sum((x_adv - images)**2, axis=(1, 2, 3))**.5
name.append(filenames)
b_i = bb_i
#exit()
# restore from pretrained
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
using_aug = True
#ckpt_path = '../tfmodels/cifar10_simple_model_epoch50'
ckpt_path = '../tfmodels/cifar10_resnet_model_epoch200'
if using_aug:
print('Using model trained by augmented data.')
ckpt_path += '_aug'
tf_model_load(sess, ckpt_path)
# prepare data
(x_train, y_train), (x_test, y_test) = load_cifar10()
x_filtered, y_filtered, _ = filter_data(sess, x, y, model, x_test, y_test)
# make sure the model load properly by running it against the test set
accuracy = validate_model(sess, x, y, model, x_filtered, y_filtered)
print('Base accuracy of the target model on legitimate images: ' + str(accuracy))
# initiate attack
target = None
attack_method = 'basic_iterative'
recover_method = 'middleground_flip'
recover_params = {'eps': 8./255,
'eps_iter': 1./255, # only used when ord=np.inf
def mnist_tutorial_adv_train(train_start=0,
train_end=60000,
test_start=0,
test_end=10000,
viz_enabled=VIZ_ENABLED,
nb_epochs=NB_EPOCHS,
batch_size=BATCH_SIZE,
source_samples=SOURCE_SAMPLES,
learning_rate=LEARNING_RATE,
attack_iterations=ATTACK_ITERATIONS,
model_path=MODEL_PATH,
targeted=TARGETED,
noise_output=NOISE_OUTPUT):
"""
MNIST tutorial for Adversarial Training
:param train_start: index of first training set example
:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
:param viz_enabled: (boolean) activate plots of adversarial examples
:param nb_epochs: number of epochs to train model
:param batch_size: size of training batches
:param nb_classes: number of output classes
:param source_samples: number of test inputs to attack
:param learning_rate: learning rate for training
:param model_path: path to the model file
:param targeted: should we run a targeted attack? or untargeted?
:return: an AccuracyReport object
"""
# Object used to keep track of (and return) key accuracies
report = AccuracyReport()
# Set TF random seed to improve reproducibility
tf.set_random_seed(1234)
# Create TF session
sess = tf.Session()
print("Created TensorFlow session.")
set_log_level(logging.DEBUG)
# Get MNIST test data
mnist = MNIST(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end)
x_train, y_train = mnist.get_set('train')
x_test, y_test = mnist.get_set('test')
# Obtain Image Parameters
img_rows, img_cols, nchannels = x_train.shape[1:4]
nb_classes = y_train.shape[1]
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, nchannels))
y = tf.placeholder(tf.float32, shape=(None, nb_classes))
nb_filters = 64
# Define TF model graph
model = ModelBasicCNN('model1', nb_classes, nb_filters)
preds = model.get_logits(x)
loss = CrossEntropy(model, smoothing=0.1)
print("Defined TensorFlow model graph.")
###########################################################################
# Training the model using TensorFlow
###########################################################################
# Train an MNIST model
train_params = {
'nb_epochs': nb_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate,
'filename': os.path.split(model_path)[-1]
}
rng = np.random.RandomState([2017, 8, 30])
# check if we've trained before, and if we have, use that pre-trained model
if os.path.exists(model_path + ".meta"):
tf_model_load(sess, model_path)
else:
train(sess, loss, x_train, y_train, args=train_params, rng=rng)
saver = tf.train.Saver()
saver.save(sess, model_path)
# Evaluate the accuracy of the MNIST model on legitimate test examples
eval_params = {'batch_size': batch_size}
accuracy = model_eval(sess, x, y, preds, x_test, y_test, args=eval_params)
assert x_test.shape[0] == test_end - test_start, x_test.shape
print('Test accuracy on legitimate test examples: {0}'.format(accuracy))
report.clean_train_clean_eval = accuracy
###########################################################################
# Craft adversarial examples using FGSM - BIM - MIM approach
###########################################################################
nb_adv_per_sample = str(nb_classes - 1) if targeted else '1'
print('Crafting ' + str(source_samples) + ' * ' + nb_adv_per_sample +
' adversarial examples')
print("This could take some time ...")
# Instantiate a CW attack object
fgsm = FastGradientMethod(model, sess=sess)
bim = BasicIterativeMethod(model, sess=sess)
mim = MomentumIterativeMethod(model, sess=sess)
if viz_enabled:
assert source_samples == nb_classes
idxs = [
np.where(np.argmax(y_test, axis=1) == i)[0][0]
for i in range(nb_classes)
]
if targeted:
if viz_enabled:
# Initialize our array for grid visualization
grid_shape = (nb_classes, 1, img_rows, img_cols, nchannels)
grid_viz_data = np.zeros(grid_shape, dtype='f')
adv_inputs = np.array([[instance] * nb_classes
for instance in x_test[idxs]],
dtype=np.float32)
else:
adv_inputs = np.array([[instance] * nb_classes
for instance in x_test[:source_samples]],
dtype=np.float32)
one_hot = np.zeros((nb_classes, nb_classes))
one_hot[np.arange(nb_classes), np.arange(nb_classes)] = 1
adv_inputs = adv_inputs.reshape(
(source_samples * nb_classes, img_rows, img_cols, nchannels))
adv_ys = np.array([one_hot] * source_samples,
dtype=np.float32).reshape(
(source_samples * nb_classes, nb_classes))
yname = "y_target"
else:
if viz_enabled:
# Initialize our array for grid visualization
grid_shape = (nb_classes, nb_classes, img_rows, img_cols,
nchannels)
grid_viz_data = np.zeros(grid_shape, dtype='f')
adv_inputs = x_test[idxs]
else:
adv_inputs = x_test[:source_samples]
adv_ys = None
yname = "y"
fgsm_params = {'eps': 0.3, 'clip_min': 0., 'clip_max': 1.}
bim_params = {
'eps': 0.3,
'clip_min': 0.,
'clip_max': 1.,
'nb_iter': 50,
'eps_iter': .01
}
mim_params = {
'eps': 0.3,
'clip_min': 0.,
'clip_max': 1.,
'nb_iter': 50,
'eps_iter': .01
}
adv_fgsm = fgsm.generate_np(adv_inputs, **fgsm_params)
adv_bim = bim.generate_np(adv_inputs, **bim_params)
adv_mim = mim.generate_np(adv_inputs, **mim_params)
eval_params = {'batch_size': np.minimum(nb_classes, source_samples)}
if targeted:
adv_fgsm_accuracy = model_eval(sess,
x,
y,
preds,
adv_fgsm,
adv_ys,
args=eval_params)
adv_bim_accuracy = model_eval(sess,
x,
y,
preds,
adv_bim,
adv_ys,
args=eval_params)
adv_mim_accuracy = model_eval(sess,
x,
y,
preds,
adv_mim,
adv_ys,
args=eval_params)
else:
if viz_enabled:
err_fgsm = model_eval(sess,
x,
y,
preds,
adv_fgsm,
y_test[idxs],
args=eval_params)
err_bim = model_eval(sess,
x,
y,
preds,
adv_bim,
y_test[idxs],
args=eval_params)
err_mim = model_eval(sess,
x,
y,
preds,
adv_mim,
y_test[idxs],
args=eval_params)
adv_fgsm_accuracy = 1 - err_fgsm
adv_bim_accuracy = 1 - err_bim
adv_mim_accuracy = 1 - err_mim
else:
err_fgsm = model_eval(sess,
x,
y,
preds,
adv_fgsm,
y_test[:source_samples],
args=eval_params)
err_bim = model_eval(sess,
x,
y,
preds,
adv_bim,
y_test[:source_samples],
args=eval_params)
err_mim = model_eval(sess,
x,
y,
preds,
adv_mim,
y_test[:source_samples],
args=eval_params)
adv_fgsm_accuracy = 1 - err_fgsm
adv_bim_accuracy = 1 - err_bim
adv_mim_accuracy = 1 - err_mim
print('--------------------------------------')
# Compute the number of adversarial examples that were successfully found
print('Avg. rate of successful adv. (FGSM) examples {0:.4f}'.format(
adv_fgsm_accuracy))
report.clean_train_adv_fgsm_eval = 1. - adv_fgsm_accuracy
print('Avg. rate of successful adv. (BIM) examples {0:.4f}'.format(
adv_bim_accuracy))
report.clean_train_adv_bim_eval = 1. - adv_bim_accuracy
print('Avg. rate of successful adv. (MIM) examples {0:.4f}'.format(
adv_mim_accuracy))
report.clean_train_adv_mim_eval = 1. - adv_mim_accuracy
# Compute the average distortion introduced by the algorithm
percent_perturbed_fgsm = np.mean(
np.sum((adv_fgsm - adv_inputs)**2, axis=(1, 2, 3))**.5)
print('Avg. L_2 norm of (FGSM) perturbations {0:.4f}'.format(
percent_perturbed_fgsm))
percent_perturbed_bim = np.mean(
np.sum((adv_bim - adv_inputs)**2, axis=(1, 2, 3))**.5)
print('Avg. L_2 norm of (BIM) perturbations {0:.4f}'.format(
percent_perturbed_bim))
percent_perturbed_mim = np.mean(
np.sum((adv_mim - adv_inputs)**2, axis=(1, 2, 3))**.5)
print('Avg. L_2 norm of (MIM) perturbations {0:.4f}'.format(
percent_perturbed_mim))
###########################################################################
# Adversarial Training
###########################################################################
model2 = ModelBasicCNN('model2', nb_classes, nb_filters)
fgsm2 = FastGradientMethod(model, sess=sess)
# bim2 = BasicIterativeMethod(model, sess=sess)
# mim2 = MomentumIterativeMethod(model, sess=sess)
def attack_fgsm(x):
return fgsm2.generate(adv_inputs, **fgsm_params)
# def attack_bim(x):
# return bim2.generate(adv_inputs, **bim_params)
# def attack_mim(x):
# return mim2.generate(adv_inputs, **mim_params)
preds2 = model2.get_logits(x)
loss2_fgsm = CrossEntropy(model2, smoothing=0.1, attack=attack_fgsm)
# loss2_bim = CrossEntropy(model2, smoothing=0.1, attack=attack_bim)
# loss2_mim = CrossEntropy(model2, smoothing=0.1, attack=attack_mim)
train(sess, loss2_fgsm, x_train, y_train, args=train_params, rng=rng)
eval_params = {'batch_size': batch_size}
accuracy = model_eval(sess, x, y, preds2, x_test, y_test, args=eval_params)
assert x_test.shape[0] == test_end - test_start, x_test.shape
print('Test accuracy on adversarial fgsm test examples: {0}'.format(
accuracy))
report.clean_train_clean_eval = accuracy
print("Defined TensorFlow model graph.")
adv_fgsm_accuracy = model_eval(sess,
x,
y,
preds,
adv_fgsm,
adv_ys,
args=eval_params)
adv_bim_accuracy = model_eval(sess,
x,
y,
preds,
adv_bim,
adv_ys,
args=eval_params)
adv_mim_accuracy = model_eval(sess,
x,
y,
preds,
adv_mim,
adv_ys,
args=eval_params)
# Close TF session
sess.close()
return report
def zoo(viz_enabled=VIZ_ENABLED,
nb_epochs=NB_EPOCHS,
batch_size=BATCH_SIZE,
source_samples=SOURCE_SAMPLES,
learning_rate=LEARNING_RATE,
attack_iterations=ATTACK_ITERATIONS,
model_path=MODEL_PATH,
targeted=TARGETED):
"""
:param viz_enabled: (boolean) activate plots of adversarial examples
:param nb_epochs: number of epochs to train model
:param batch_size: size of training batches
:param nb_classes: number of output classes
:param source_samples: number of test inputs to attack
:param learning_rate: learning rate for training
:param model_path: path to the model file
:param targeted: should we run a targeted attack? or untargeted?
:return: an AccuracyReport object
"""
# Object used to keep track of (and return) key accuracies
report = AccuracyReport()
# Set TF random seed to improve reproducibility
tf.set_random_seed(1234)
# Create TF session
sess = tf.Session()
print("Created TensorFlow session.")
set_log_level(logging.DEBUG)
if DATASET == 'MNIST':
train_start = 0
train_end = 60000
test_start = 0
test_end = 10000
ds = dataset.MNIST(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end,
center=False)
elif DATASET == 'SVHN':
train_start = 0
train_end = 73257
test_start = 0
test_end = 26032
ds = dataset.SVHN(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end)
elif DATASET == 'CIFAR10':
train_start = 0
train_end = 60000
test_start = 0
test_end = 10000
ds = dataset.CIFAR10(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end,
center=False)
x_train, y_train, x_test, y_test = ds.get_set('train') + ds.get_set('test')
# Obtain Image Parameters
img_rows, img_cols, nchannels = x_train.shape[1:4]
nb_classes = y_train.shape[1]
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, nchannels))
y = tf.placeholder(tf.float32, shape=(None, nb_classes))
nb_filters = 64
# Define TF model graph
model = ModelBasicCNN(DATASET, nb_classes, nb_filters,
(None, img_rows, img_cols, nchannels))
preds = model.get_logits(x)
loss = CrossEntropy(model, smoothing=0.1)
print("Defined TensorFlow model graph.")
###########################################################################
# Training the model using TensorFlow
###########################################################################
# Train an MNIST model
train_params = {
'nb_epochs': nb_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate,
'filename': os.path.split(model_path)[-1]
}
rng = np.random.RandomState([2018, 10, 22])
# check if we've trained before, and if we have, use that pre-trained model
if os.path.exists(model_path + ".meta"):
tf_model_load(sess, model_path)
else:
train(sess, loss, x, y, x_train, y_train, args=train_params, rng=rng)
saver = tf.train.Saver()
saver.save(sess, model_path)
# Evaluate the accuracy of the MNIST model on legitimate test examples
eval_params = {'batch_size': batch_size}
accuracy = model_eval(sess, x, y, preds, x_test, y_test, args=eval_params)
assert x_test.shape[0] == test_end - test_start, x_test.shape
print('Test accuracy on legitimate test examples: {0}'.format(accuracy))
report.clean_train_clean_eval = accuracy
###########################################################################
# Craft adversarial examples using Carlini and Wagner's approach
###########################################################################
nb_adv_per_sample = str(nb_classes - 1) if targeted else '1'
print('Crafting ' + str(source_samples) + ' * ' + nb_adv_per_sample +
' adversarial examples')
print("This could take some time ...")
# Instantiate a Zoo attack object
zoo = Zoo(model, sess=sess)
if viz_enabled:
assert source_samples == nb_classes
idxs = [
np.where(np.argmax(y_test, axis=1) == i)[0][0]
for i in range(nb_classes)
]
if targeted:
if viz_enabled:
# Initialize our array for grid visualization
grid_shape = (nb_classes, nb_classes, img_rows, img_cols,
nchannels)
grid_viz_data = np.zeros(grid_shape, dtype='f')
adv_inputs = np.array([[instance] * nb_classes
for instance in x_test[idxs]],
dtype=np.float32)
else:
adv_inputs = np.array([[instance] * nb_classes
for instance in x_test[:source_samples]],
dtype=np.float32)
one_hot = np.zeros((nb_classes, nb_classes))
one_hot[np.arange(nb_classes), np.arange(nb_classes)] = 1
adv_inputs = adv_inputs.reshape(
(source_samples * nb_classes, img_rows, img_cols, nchannels))
adv_ys = np.array([one_hot] * source_samples,
dtype=np.float32).reshape(
(source_samples * nb_classes, nb_classes))
yname = "y_target"
else:
if viz_enabled:
# Initialize our array for grid visualization
grid_shape = (nb_classes, 2, img_rows, img_cols, nchannels)
grid_viz_data = np.zeros(grid_shape, dtype='f')
adv_inputs = x_test[idxs]
else:
adv_inputs = x_test[:source_samples]
adv_ys = None
yname = "y"
zoo_params = {
'binary_search_steps': BINARY_SEARCH_STEPS,
yname: adv_ys,
'max_iterations': attack_iterations,
'learning_rate': ZOO_LEARNING_RATE,
'batch_size':
source_samples * nb_classes if targeted else source_samples,
'initial_const': INIT_CONST,
'solver': SOLVER,
'image_shape': [img_rows, img_cols, nchannels],
'nb_classes': nb_classes
}
adv = zoo.generate_np(adv_inputs, **zoo_params)
eval_params = {'batch_size': np.minimum(nb_classes, source_samples)}
if targeted:
adv_accuracy = model_eval(sess,
x,
y,
preds,
adv,
adv_ys,
args=eval_params)
else:
if viz_enabled:
adv_accuracy = 1 - model_eval(
sess, x, y, preds, adv, y_test[idxs], args=eval_params)
else:
adv_accuracy = 1 - model_eval(sess,
x,
y,
preds,
adv,
y_test[:source_samples],
args=eval_params)
if viz_enabled:
for j in range(nb_classes):
if targeted:
for i in range(nb_classes):
grid_viz_data[i, j] = adv[i * nb_classes + j]
else:
grid_viz_data[j, 0] = adv_inputs[j]
grid_viz_data[j, 1] = adv[j]
print(grid_viz_data.shape)
print('--------------------------------------')
# Compute the number of adversarial examples that were successfully found
print('Avg. rate of successful adv. examples {0:.4f}'.format(adv_accuracy))
report.clean_train_adv_eval = 1. - adv_accuracy
# Compute the average distortion introduced by the algorithm
percent_perturbed = np.mean(
np.sum((adv - adv_inputs)**2, axis=(1, 2, 3))**.5)
print('Avg. L_2 norm of perturbations {0:.4f}'.format(percent_perturbed))
# Close TF session
sess.close()
# Finally, block & display a grid of all the adversarial examples
if viz_enabled:
_ = grid_visual(grid_viz_data)
return report
def cifar10_tutorial(train_start=0,
train_end=50000,
test_start=0,
test_end=10000,
nb_epochs=NB_EPOCHS,
batch_size=BATCH_SIZE,
learning_rate=LEARNING_RATE,
clean_train=CLEAN_TRAIN,
testing=False,
backprop_through_attack=BACKPROP_THROUGH_ATTACK,
nb_filters=NB_FILTERS,
num_threads=None,
label_smoothing=0.1):
"""
CIFAR10 cleverhans tutorial
:param train_start: index of first training set example
:param train_end: index of last training set example
:param test_start: index of first test set example
:param test_end: index of last test set example
:param nb_epochs: number of epochs to train model
:param batch_size: size of training batches
:param learning_rate: learning rate for training
:param clean_train: perform normal training on clean examples only
before performing adversarial training.
:param testing: if true, complete an AccuracyReport for unit tests
to verify that performance is adequate
:param backprop_through_attack: If True, backprop through adversarial
example construction process during
adversarial training.
:param label_smoothing: float, amount of label smoothing for cross entropy
:return: an AccuracyReport object
"""
# Object used to keep track of (and return) key accuracies
report = AccuracyReport()
# Set TF random seed to improve reproducibility
tf.set_random_seed(1234)
# Set logging level to see debug information
set_log_level(logging.DEBUG)
# Create TF session
if num_threads:
config_args = dict(intra_op_parallelism_threads=1)
else:
config_args = {}
sess = tf.Session(config=tf.ConfigProto(**config_args))
# Get CIFAR10 data
data = CIFAR10(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end)
dataset_size = data.x_train.shape[0]
dataset_train = data.to_tensorflow()[0]
dataset_train = dataset_train.map(
lambda x, y: (random_shift(random_horizontal_flip(x)), y), 4)
dataset_train = dataset_train.batch(batch_size)
dataset_train = dataset_train.prefetch(16)
x_train, y_train = data.get_set('train')
x_test, y_test = data.get_set('test')
# Use Image Parameters
img_rows, img_cols, nchannels = x_test.shape[1:4]
nb_classes = y_test.shape[1]
# Define input TF placeholder
x = tf.placeholder(tf.float32, shape=(None, img_rows, img_cols, nchannels))
y = tf.placeholder(tf.float32, shape=(None, nb_classes))
# Train an MNIST model
train_params = {
'nb_epochs': nb_epochs,
'batch_size': batch_size,
'learning_rate': learning_rate
}
eval_params = {'batch_size': batch_size}
fgsm_params = {'eps': 0.13, 'clip_min': 0., 'clip_max': 1.}
rng = np.random.RandomState([2017, 8, 30])
def do_eval(preds, x_set, y_set, report_key, is_adv=None):
acc = model_eval(sess, x, y, preds, x_set, y_set, args=eval_params)
setattr(report, report_key, acc)
if is_adv is None:
report_text = None
elif is_adv:
report_text = 'adversarial'
else:
report_text = 'legitimate'
if report_text:
print('Test accuracy on %s examples: %0.4f' % (report_text, acc))
model = ModelAllConvolutional('model1',
nb_classes,
nb_filters,
input_shape=[32, 32, 3])
preds = model.get_logits(x)
if clean_train:
loss = CrossEntropy(model, smoothing=label_smoothing)
def evaluate():
do_eval(preds, x_test, y_test, 'clean_train_clean_eval', False)
train(sess,
loss,
None,
None,
dataset_train=dataset_train,
dataset_size=dataset_size,
evaluate=evaluate,
args=train_params,
rng=rng,
var_list=model.get_params())
# save model
#saver = tf.train.Saver()
#saver.save(sess, "./checkpoint_dir/clean_model_100.ckpt")
# load model and compute testing accuracy
if testing:
tf_model_load(sess, file_path="./checkpoint_dir/clean_model_100.ckpt")
do_eval(preds, x_test, y_test, 'clean_train_clean_eval', False)
# Initialize the Fast Gradient Sign Method (FGSM) attack object and
# graph
fgsm = FastGradientMethod(model, sess=sess)
adv_x = fgsm.generate(x, **fgsm_params)
preds_adv = model.get_logits(adv_x)
# Evaluate the accuracy of the CIFAR10 model on adversarial examples
do_eval(preds_adv, x_test, y_test, 'clean_train_adv_eval', True)
# generate and show adversarial samples
x_test_adv = np.zeros(shape=x_test.shape)
for i in range(10):
x_test_adv[i * 1000:(i + 1) * 1000] = adv_x.eval(
session=sess, feed_dict={x: x_test[i * 1000:(i + 1) * 1000]})
# implement anisotropic diffusion on adversarial samples
x_test_filtered = np.zeros(shape=x_test_adv.shape)
for i in range(y_test.shape[0]):
x_test_filtered[i] = filter.anisotropic_diffusion(x_test_adv[i])
# implement median on adversarial samples
# x_test_filtered_med = np.zeros(shape=x_test_adv.shape)
# for i in range(y_test.shape[0]):
# x_test_filtered_med[i] = medfilt(x_test_filtered_ad[i], kernel_size=(3,3,1))
acc = model_eval(sess,
x,
y,
preds,
x_test_filtered,
y_test,
args=eval_params)
print("acc after anisotropic diffusion is {}".format(acc))
return report
