def main(_):
batch_size = FLAGS.batch_size
batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
with tf.Graph().as_default():
# Prepare graph
x_input = tf.placeholder(tf.float32, shape=batch_shape)
with tf.Session() as sess:
model = InceptionV3Model(sess=sess)
model._build()
mim = MIM(model, back='tf', sess=None)
mim_params = {'eps_iter': 0.06,
'eps': 0.3,
'nb_iter': 10,
'ord': 2,
'decay_factor': 1.0}
x_adv = mim.generate(x_input, **mim_params)
j = 0
z_samples = np.zeros((10000, 299, 299, 3))
real_samples = np.zeros((10000, 299, 299, 3))
for filenames, images in load_images(FLAGS.input_dir, batch_shape):
adv_images = sess.run(x_adv, feed_dict={x_input: images})
for (real, adv) in zip(images, adv_images):
z_samples[j] = adv
real_samples[j] = real
#show(real, adv, model, sess)
j += 1
if not (j % 100):
print j
if j >= 5000:
print "Max examples exceeded, early stopping"
break
save_npy(real_samples, z_samples)
def main(_):
batch_size = FLAGS.batch_size
batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
num_classes = 1001
targeted = False
tf.logging.set_verbosity(tf.logging.DEBUG)
with tf.Graph().as_default():
x_input = tf.placeholder(tf.float32, shape=batch_shape)
model = InceptionModel(num_classes)
with tf.Session() as sess:
predictions = model(x_input)
mim = MIM(model, back='tf', sess=None)
mim_params = {
'eps_iter': 0.06,
'eps': 0.3,
'nb_iter': 10,
'ord': 2,
'decay_factor': 1.0
}
x_adv = mim.generate(x_input, **mim_params)
sys.exit(0)
saver = tf.train.Saver(slim.get_model_variables())
saver.restore(sess, FLAGS.checkpoint_path)
z_samples = np.zeros((10000, 299, 299, 3))
real_samples = np.zeros((10000, 299, 299, 3))
meta_graph_def = tf.train.export_meta_graph(
filename='tmp/imagenet/inception_v3.meta')
saver.save(sess, 'tmp/imagenet/inception_v3.ckpt')
#freeze_graph(sess)
sys.exit(0)
"""
a = [n.name for n in tf.get_default_graph().as_graph_def().node]
for item in a:
print item
"""
j = 0
for filenames, images in load_images(FLAGS.input_dir, batch_shape):
adv_images = sess.run(x_adv, feed_dict={x_input: images})
for (real, adv) in zip(images, adv_images):
z_samples[j] = adv
real_samples[j] = real
j += 1
if not (j % 100):
print j
if j >= 5000:
print "Max examples exceeded, early stopping"
break
save_npy(real_samples, z_samples)
def main(_):
# Images for inception classifier are normalized to be in [-1, 1] interval,
# eps is a difference between pixels so it should be in [0, 2] interval.
# Renormalizing epsilon from [0, 255] to [0, 2].
batch_size = FLAGS.batch_size
batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
num_classes = 1001
targeted = False
tf.logging.set_verbosity(tf.logging.DEBUG)
with tf.Graph().as_default():
# Prepare graph
x_input = tf.placeholder(tf.float32, shape=batch_shape)
model = InceptionModel(num_classes)
with tf.Session() as sess:
mim = MIM(model, back='tf', sess=None)
mim_params = {
'eps_iter': 0.06,
'eps': 0.3,
'nb_iter': 10,
'ord': 2,
'decay_factor': 1.0
}
x_adv = mim.generate(x_input, **mim_params)
saver = tf.train.Saver(slim.get_model_variables())
session_creator = tf.train.ChiefSessionCreator(
scaffold=tf.train.Scaffold(saver=saver),
checkpoint_filename_with_path=FLAGS.checkpoint_path,
master=FLAGS.master)
saver.restore(sess, FLAGS.checkpoint_path)
sess.run(tf.global_variables_initializer())
# with tf.train.MonitoredSession(session_creator=session_creator) as sess:
i = 0
for filenames, images in load_images(FLAGS.input_dir, batch_shape):
adv_images = sess.run(x_adv, feed_dict={x_input: images})
print "input images: ", images.shape
#adv_images = cw.generate_np(images, **cw_params)
i += 16
print i
# print filenames
# print adv_images.shape
# adv_images = cw.generate_np(
save_images(adv_images, filenames, FLAGS.output_dir)
def main(_):
# Images for inception classifier are normalized to be in [-1, 1] interval,
# eps is a difference between pixels so it should be in [0, 2] interval.
# Renormalizing epsilon from [0, 255] to [0, 2].
eps = 2.0 * FLAGS.max_epsilon / 255.0
batch_shape = [FLAGS.batch_size, FLAGS.image_height, FLAGS.image_width, 3]
num_classes = 1001
tf.logging.set_verbosity(tf.logging.INFO)
with tf.Graph().as_default():
# Prepare graph
x_input = tf.placeholder(tf.float32, shape=batch_shape)
model = InceptionModel(num_classes)
mim = MIM(model, back='tf', sess=None)
mim_params = {
'eps_iter': 0.06,
'eps': 0.3,
'nb_iter': 10,
'ord': 2,
'decay_factor': 1.0
}
x_adv = mim.generate(x_input, **mim_params)
# Run computation
saver = tf.train.Saver(slim.get_model_variables())
session_creator = tf.train.ChiefSessionCreator(
scaffold=tf.train.Scaffold(saver=saver),
checkpoint_filename_with_path=FLAGS.checkpoint_path,
master=FLAGS.master)
with tf.train.MonitoredSession(
session_creator=session_creator) as sess:
for filenames, images in load_images(FLAGS.input_dir, batch_shape):
adv_images = sess.run(x_adv, feed_dict={x_input: images})
save_images(adv_images, filenames, FLAGS.output_dir)
def main(argv=None):
"""
CIFAR10 CleverHans tutorial
:return:
"""
# 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.")
if keras.backend.image_dim_ordering() != 'tf':
keras.backend.set_image_dim_ordering('tf')
# Create TF session and set as Keras backend session
sess = tf.Session()
keras.backend.set_session(sess)
# 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, 32, 32, 3))
y = tf.placeholder(tf.float32, shape=(None, 10))
# Define TF model graph
model = cnn_model(img_rows=32, img_cols=32, channels=3)
predictions = model(x)
print "Defined TensorFlow model graph."
def evaluate():
# Evaluate the accuracy of the CIFAR10 model on legitimate test
# examples
eval_params = {'batch_size': FLAGS.batch_size}
accuracy = model_eval(sess,
x,
y,
predictions,
X_test,
Y_test,
args=eval_params)
assert X_test.shape[0] == 10000, X_test.shape
print 'Test accuracy on legitimate test examples: ' + str(accuracy)
# Train an CIFAR10 model
train_params = {
'nb_epochs': FLAGS.nb_epochs,
'batch_size': FLAGS.batch_size,
'learning_rate': FLAGS.learning_rate
}
model_train(sess,
x,
y,
predictions,
X_train,
Y_train,
evaluate=evaluate,
args=train_params)
# Craft adversarial examples using Fast Gradient Sign Method (FGSM)
# adv_x = fgsm(x, predictions, eps=0.3)
mim = MIM(model, back='tf', sess=sess)
mim_params = {
'eps_iter': 0.06,
'eps': 0.3,
'nb_iter': 10,
'ord': 2,
'decay_factor': 1.0
}
adv_x = mim.generate(x, **mim_params)
eval_params = {'batch_size': FLAGS.batch_size}
X_test_adv, = batch_eval(sess, [x], [adv_x], [X_test], args=eval_params)
assert X_test_adv.shape[0] == 10000, X_test_adv.shape
accuracy = model_eval(sess,
x,
y,
predictions,
X_test_adv,
Y_test,
args=eval_params)
print 'Test accuracy on adversarial examples: ' + str(accuracy)
from scipy.misc import imsave
path = '/home/neale/repos/adversarial-toolbox/images/adversarials/mim/cifar/symmetric/'
"""
for i, (real, adv) in enumerate(zip(X_test, X_test_adv)):
imsave(path+'adv/adv_{}.png'.format(i), adv)
"""
preds = model_argmax(sess, x, predictions, X_test_adv)
print Y_test.shape
print preds.shape
count = 0
for i in range(len(preds)):
if np.argmax(Y_test[i]) == preds[i]:
# imsave(path+'real/im_{}.png'.format(i), X_test[i])
# imsave(path+'adv/adv_{}.png'.format(i), X_test_adv[i])
count += 1
print "saved ", count