def main(_):
with tf.Session() as sess:
K.set_session(sess)
if FLAGS.dataset == 'MNIST':
data, model = MNIST(), MNISTModel("models/mnist", sess)
elif FLAGS.datset == 'Cifar':
data, model = CIFAR(), CIFARModel("models/cifar", sess)
def _model_fn(x, logits=False):
ybar, logits_ = model.predict(x)
if logits:
return ybar, logits_
return ybar
if FLAGS.dataset == 'MNIST':
x_adv = fgsm(_model_fn, x, epochs=9, eps=0.02)
elif FLAGS.datset == 'Cifar':
x_adv = fgsm(_model_fn, x, epochs=4, eps=0.01)
X_adv_test = attack(x_adv, data.test_data, data.test_labels, sess)
X_adv_train = attack(x_adv, data.train_data, data.train_labels, sess)
np.save('adversarial_outputs/fgsm_train_' + FLAGS.dataset.lower() + '.npy', X_adv_train)
np.save('adversarial_outputs/fgsm_test_' + FLAGS.dataset.lower() + '.npy', X_adv_test)
print("Legit/Adversarial training set")
model.evaluate(data.train_data, data.train_labels)
model.evaluate(X_adv_train, data.train_labels)
print("Legit/Adversarial test set")
model.evaluate(data.test_data, data.test_labels)
model.evaluate(X_adv_test, data.test_labels)
def main():
data, model = MNIST(), MNISTModel(restore="models/mnist", use_log=True)
origImgs, origLabels, origImgID = util.generate_attack_data_set(data, model, MGR)
delImgAT_Init = np.zeros(origImgs[0].shape)
objfunc = ObjectiveFunc.OBJFUNC(MGR, model, origImgs, origLabels)
MGR.Add_Parameter('eta', MGR.parSet['alpha']/origImgs[0].size)
MGR.Log_MetaData()
if(MGR.parSet['optimizer'] == 'ZOSVRG'):
delImgAT = svrg.ZOSVRG(delImgAT_Init, MGR, objfunc)
elif(MGR.parSet['optimizer'] == 'ZOSGD'):
delImgAT = sgd.ZOSGD(delImgAT_Init, MGR, objfunc)
else:
print('Please specify a valid optimizer')
for idx_ImgID in range(MGR.parSet['nFunc']):
currentID = origImgID[idx_ImgID]
orig_prob = model.model.predict(np.expand_dims(origImgs[idx_ImgID], axis=0))
advImg = np.tanh(np.arctanh(origImgs[idx_ImgID]*1.9999999)+delImgAT)/2.0
adv_prob = model.model.predict(np.expand_dims(advImg, axis=0))
suffix = "id{}_Orig{}_Adv{}".format(currentID, np.argmax(orig_prob), np.argmax(adv_prob))
util.save_img(advImg, "{}/Adv_{}.png".format(MGR.parSet['save_path'], suffix))
util.save_img(np.tanh(delImgAT)/2.0, "{}/Delta.png".format(MGR.parSet['save_path']))
sys.stdout.flush()
MGR.logHandler.close()
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 run_pca(Data, num_components=10, invert=False):
data = Data()
sess = K.get_session()
K.set_learning_phase(False)
shape = (-1, 784)
pca = sklearn.decomposition.PCA(n_components=num_components)
pca.fit(data.train_data.reshape(shape)) # [:10000]
if invert:
model = MNISTModel("models/mnist-pca-cnn-top-"+str(num_components))
else:
model = make_model(num_components)
model.load_weights("models/mnist-pca-top-"+str(num_components))
model = Wrap(model,pca)
tf_mean = tf.constant(pca.mean_,dtype=tf.float32)
tf_components = tf.constant(pca.components_.T,dtype=tf.float32)
def new_predict(xs):
# map to PCA space
xs = tf.reshape(xs,(-1,784))
xs -= tf_mean
xs = tf.matmul(xs, tf_components)
# map back
xs = tf.matmul(xs, tf.transpose(tf_components))
xs += tf_mean
xs = tf.reshape(xs, (-1, 28, 28, 1))
return model.model(xs)
if invert:
model.predict = new_predict
attack = CarliniL2(sess, model, batch_size=100, max_iterations=3000,
binary_search_steps=6, targeted=False,
initial_const=1)
N = 100
test_adv = attack.attack(data.test_data[:N], data.test_labels[:N])
print('accuracy',np.mean(np.argmax(sess.run(model.predict(tf.constant(data.test_data,dtype=np.float32))),axis=1)==np.argmax(data.test_labels,axis=1)))
print(list(test_adv[0].flatten()))
print('dist',np.mean(np.sum((test_adv-data.test_data[:N])**2,axis=(1,2,3))**.5))
it = np.argmax(sess.run(model.predict(tf.constant(test_adv))),axis=1)
print('success',np.mean(it==np.argmax(data.test_labels,axis=1)[:N]))
def compare_baseline():
data = MNIST()
model = MNISTModel("models/mnist")
sess = K.get_session()
attack = CarliniL2(sess, model, batch_size=100, max_iterations=3000,
binary_search_steps=4, targeted=False,
initial_const=10)
N = 100
test_adv = attack.attack(data.test_data[:N], data.test_labels[:N])
print('dist',np.mean(np.sum((test_adv-data.test_data[:N])**2,axis=(1,2,3))**.5))
def train(data, file_name, components=100, num_epochs=20, batch_size=256, pca=None, invert=False):
"""
Standard neural network training procedure.
"""
shape = (-1, data.train_data.shape[1]*data.train_data.shape[2]*data.train_data.shape[3])
train_data = pca.transform(data.train_data.reshape(shape))[:,:components]
validation_data = pca.transform(data.validation_data.reshape(shape))[:,:components]
test_data = pca.transform(data.test_data.reshape(shape))[:,:components]
print(train_data.shape)
if invert:
train_data = pca.inverse_transform(train_data).reshape((-1, 28, 28, 1))
validation_data = pca.inverse_transform(validation_data).reshape((-1, 28, 28, 1))
test_data = pca.inverse_transform(test_data).reshape((-1, 28, 28, 1))
model = MNISTModel(None).model
else:
model = make_model(components)
def fn(correct, predicted):
return tf.nn.softmax_cross_entropy_with_logits(labels=correct,
logits=predicted)
#sgd = SGD(lr=0.1, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss=fn,
optimizer='adam',
metrics=['accuracy'])
model.fit(train_data, data.train_labels,
batch_size=batch_size,
validation_data=(validation_data, data.validation_labels),
nb_epoch=num_epochs,
shuffle=True)
acc = np.mean(np.argmax(model.predict(test_data),axis=1)==np.argmax(data.test_labels,axis=1))
print("Overall accuracy on test set:", acc)
if file_name != None:
model.save(file_name)
return model
def main(args):
with tf.Session() as sess:
print("Loading data and classification model: {}".format(
args["dataset"]))
if args['dataset'] == "mnist":
data, model = MNIST(), MNISTModel("models/mnist",
sess,
use_softmax=True)
elif args['dataset'] == "cifar10":
data, model = CIFAR(), CIFARModel("models/cifar",
sess,
use_softmax=True)
elif args['dataset'] == "imagenet":
# data, model = ImageNet(data_path=args["imagenet_dir"], targetFile=args["attack_single_img"]), InceptionModel(sess, use_softmax=True)
data, model = ImageNetDataNP(), InceptionModel(sess,
use_softmax=True)
# elif args['dataset'] == "imagenet_np":
if len(data.test_labels) < args["num_img"]:
raise Exception("No enough data, only have {} but need {}".format(
len(data.test_labels), args["num_img"]))
if args["attack_single_img"]:
# manually setup attack set
# attacking only one image with random attack]
orig_img = data.test_data
orig_labels = data.test_labels
orig_img_id = np.array([1])
if args["attack_type"] == "targeted":
target_labels = [
np.eye(model.num_labels)[args["single_img_target_label"]]
]
else:
target_labels = orig_labels
else:
# generate attack set
if args["dataset"] == "imagenet" or args[
"dataset"] == "imagenet_np":
shift_index = True
else:
shift_index = False
if args["random_target"] and (args["dataset"] == "imagenet"
or args["dataset"] == "imagenet_np"):
# find all possible class
all_class = np.unique(np.argmax(data.test_labels, 1))
all_orig_img, all_target_labels, all_orig_labels, all_orig_img_id = util.generate_attack_data_set(
data,
args["num_img"],
args["img_offset"],
model,
attack_type=args["attack_type"],
random_target_class=all_class,
shift_index=shift_index)
elif args["random_target"]:
# random target on all possible classes
class_num = data.test_labels.shape[1]
all_orig_img, all_target_labels, all_orig_labels, all_orig_img_id = util.generate_attack_data_set(
data,
args["num_img"],
args["img_offset"],
model,
attack_type=args["attack_type"],
random_target_class=list(range(class_num)),
shift_index=shift_index)
else:
all_orig_img, all_target_labels, all_orig_labels, all_orig_img_id = util.generate_attack_data_set(
data,
args["num_img"],
args["img_offset"],
model,
attack_type=args["attack_type"],
shift_index=shift_index)
# check attack data
# for i in range(len(orig_img_id)):
# tar_lab = np.argmax(target_labels[i])
# orig_lab = np.argmax(orig_labels[i])
# print("{}:, target label:{}, orig_label:{}, orig_img_id:{}".format(i, tar_lab, orig_lab, orig_img_id[i]))
# attack related settings
if args["attack_method"] == "zoo" or args[
"attack_method"] == "autozoom_bilin":
if args["img_resize"] is None:
args["img_resize"] = model.image_size
print(
"Argument img_resize is not set and not using autoencoder, set to image original size:{}"
.format(args["img_resize"]))
if args["attack_method"] == "zoo" or args["attack_method"] == "zoo_ae":
if args["batch_size"] is None:
args["batch_size"] = 128
print(
"Using zoo or zoo_ae attack, and batch_size is not set.\nSet batch_size to {}."
.format(args["batch_size"]))
else:
if args["batch_size"] is not None:
print("Argument batch_size is not used")
args["batch_size"] = 1 # force to be 1
if args["attack_method"] == "zoo_ae" or args[
"attack_method"] == "autozoom_ae":
#_, decoder = util.load_codec(args["codec_prefix"])
if args["dataset"] == "mnist" or args["dataset"] == "cifar10":
codec = CODEC(model.image_size,
model.num_channels,
args["compress_mode"],
use_tanh=False)
else:
codec = CODEC(128, model.num_channels, args["compress_mode"])
print(args["codec_prefix"])
codec.load_codec(args["codec_prefix"])
decoder = codec.decoder
print(decoder.input_shape)
args["img_resize"] = decoder.input_shape[1]
print("Using autoencoder, set the attack image size to:{}".format(
args["img_resize"]))
# setup attack
if args["attack_method"] == "zoo":
blackbox_attack = ZOO(sess, model, args)
elif args["attack_method"] == "zoo_ae":
blackbox_attack = ZOO_AE(sess, model, args, decoder)
elif args["attack_method"] == "autozoom_bilin":
blackbox_attack = AutoZOOM_BiLIN(sess, model, args)
elif args["attack_method"] == "autozoom_ae":
blackbox_attack = AutoZOOM_AE(sess, model, args, decoder, codec)
save_prefix = os.path.join(args["save_path"], args["dataset"],
args["attack_method"], args["attack_type"])
os.system("mkdir -p {}".format(save_prefix))
total_success = 0
l2_total = 0
for i in range(all_orig_img_id.size):
orig_img = all_orig_img[i:i + 1]
target = all_target_labels[i:i + 1]
label = all_orig_labels[i:i + 1]
target_class = np.argmax(target)
true_class = np.argmax(label)
test_index = all_orig_img_id[i]
# print information
print(
"[Info][Start]{}: test_index:{}, true label:{}, target label:{}"
.format(i, test_index, true_class, target_class))
if args["attack_method"] == "zoo_ae" or args[
"attack_method"] == "autozoom_ae":
#print ae info
if args["dataset"] == "mnist" or args["dataset"] == "cifar10":
temp_img = all_orig_img[i:i + 1]
else:
temp_img = all_orig_img[i]
temp_img = (temp_img + 0.5) * 255
temp_img = scipy.misc.imresize(temp_img, (128, 128))
temp_img = temp_img / 255 - 0.5
temp_img = np.expand_dims(temp_img, axis=0)
encode_img = codec.encoder.predict(temp_img)
decode_img = codec.decoder.predict(encode_img)
diff_img = (decode_img - temp_img)
diff_mse = np.mean(diff_img.reshape(-1)**2)
print("[Info][AE] MSE:{:.4f}".format(diff_mse))
timestart = time.time()
adv_img = blackbox_attack.attack(orig_img, target)
timeend = time.time()
if len(adv_img.shape) == 3:
adv_img = np.expand_dims(adv_img, axis=0)
l2_dist = np.sum((adv_img - orig_img)**2)**.5
adv_class = np.argmax(model.model.predict(adv_img))
success = False
if args["attack_type"] == "targeted":
if adv_class == target_class:
success = True
else:
if adv_class != true_class:
success = True
if success:
total_success += 1
l2_total += l2_dist
print(
"[Info][End]{}: test_index:{}, true label:{}, adv label:{}, success:{}, distortion:{:.5f}, success_rate:{:.4f}, l2_avg:{:.4f}"
.format(i, test_index, true_class, adv_class, success, l2_dist,
total_success / (i + 1),
0 if total_success == 0 else l2_total / total_success))
# save images
suffix = "id{}_testIndex{}_true{}_adv{}".format(
i, test_index, true_class, adv_class)
# original image
save_name = os.path.join(save_prefix, "Orig_{}.png".format(suffix))
util.save_img(orig_img, save_name)
save_name = os.path.join(save_prefix, "Orig_{}.npy".format(suffix))
np.save(save_name, orig_img)
# adv image
save_name = os.path.join(save_prefix, "Adv_{}.png".format(suffix))
util.save_img(adv_img, save_name)
save_name = os.path.join(save_prefix, "Adv_{}.npy".format(suffix))
np.save(save_name, adv_img)
# diff image
save_name = os.path.join(save_prefix, "Diff_{}.png".format(suffix))
util.save_img((adv_img - orig_img) / 2, save_name)
save_name = os.path.join(save_prefix, "Diff_{}.npy".format(suffix))
np.save(save_name, adv_img - orig_img)
def main():
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
train_data = mnist.train.images * 2.0 - 1.0
train_label = mnist.train.labels
test_data = mnist.test.images * 2.0 - 1.0
test_label = mnist.test.labels
x_dim = train_data.shape[1]
y_dim = train_label.shape[1]
opt = opts.parse_opt()
batch_size = opt.batch_size
# Changing the options here.
opt.input_data = "MNIST"
opt.input_c_dim = 1
opt.output_c_dim = 1
opt.input_dim = x_dim
opt.label_dim = y_dim
# Running arguments
opt.c = 1.
opt.ld = 500.
opt.H_lambda = 10.
opt.cgan_flag = True
opt.patch_flag = True
opt.G_lambda = 10.
opt.s_l = 0
opt.t_l = 1
# batch_size = opt.batch_size
# Runnign a session, to load the saved model.
with tf.Session() as sess:
model_store = opt.model_restore
print 'MNIST model is stored at %s' % model_store
whitebox_model = MNISTModel(model_store)
#initial ADVGAN
model = advGAN(whitebox_model, model_store, opt, sess)
best_model_path = './GAN/save/best.ckpt'
print 'advGAN is stored at %s' % best_model_path
model.load(best_model_path)
# tvars = tf.trainable_variables()
# tvars_vals = sess.run(tvars)
# for var, val in zip(tvars, tvars_vals):
# if 'generator' not in var.name:
# continue
# print(var.name, val.shape) # Prints the name of the variable alongside its value.
# We have to load a batch of images, then create the fake ones.
# They should be identical.
num_images = 10
images = train_data[:num_images]
fake_images = sess.run([model.fake_images_sample],
{model.source: images})
plt.imshow(np.reshape(fake_images[0], [28, 28]))
plt.show()
def train():
flatten_flag = True # flatten output of G or not?
opt = opts.parse_opt()
opt.input_data = "MNIST"
# mapping [0,1] -> [-1,1]
# load data
# mnist = input_data.read_data_sets('MNIST_data', one_hot=True)
# train_data = mnist.train.images * 2.0 - 1.0
# train_label = mnist.train.labels
# test_data = mnist.test.images * 2.0 - 1.0
# test_label = mnist.test.labels
loaded = np.load('MNIST_data/B.npz')
train_data, train_label, test_data, test_label = \
loaded['train_data'], loaded['train_label'], \
loaded['test_data'], loaded['test_label']
# We create the label clues here.
if opt.cgan_gen is True:
label_clue = np.zeros((train_label.shape[1], opt.img_dim, opt.img_dim,
train_label.shape[1]))
for lbl in range(train_label.shape[1]):
label_clue[lbl, :, :, lbl] = 1
if opt.cgan_gen:
output_samples, output_labels = output_sample(test_data, test_label,
True)
else:
output_samples = output_sample(test_data, test_label)
print output_samples.shape
print 'Shape of data:'
print '\tTraining data: ' + str(train_data.shape)
print '\tTraining label: ' + str(train_label.shape)
print '\tTest data: ' + str(test_data.shape)
print '\tTest label: ' + str(test_label.shape)
x_dim = train_data.shape[1]
y_dim = train_label.shape[1]
opt.input_c_dim = 1
opt.output_c_dim = 1
opt.input_dim = x_dim
opt.label_dim = y_dim
batch_size = opt.batch_size
NUM_THREADS = 2
tf_config = tf.ConfigProto()
tf_config.intra_op_parallelism_threads = NUM_THREADS
tf_config.gpu_options.allow_growth = True
with tf.Session(config=tf_config) as sess:
# Initialize the variables, and restore the variables form checkpoint if there is.
# and initialize the writer
global_step = 0
print '\tRetrieving evil model from "%s"' % opt.evil_model_path
evil_model = MNISTModel(opt.evil_model_path)
print '\tRetrieving good model from "%s"' % opt.good_model_path
good_model = OddEvenMNIST(opt.good_model_path)
# model = advGAN(whitebox_model, model_store, opt, sess)
model = advGAN(good_model, evil_model, opt, sess)
min_adv_accuracy = 10e10
max_accuracy_diff = -np.inf
# summary_dir = "logs/MNIST/g_%d_ld_%d_gl_%d_L2_%.2f_dn_%d" % (
# opt.G_lambda, opt.ld, opt.good_loss_coeff,
# opt.L2_lambda, opt.d_train_num)
summary_dir = "logs/MNIST/dn_%d_gn_%d" % (opt.d_train_num,
opt.g_train_num)
duplicate_num = 0
while os.path.isdir(summary_dir + '_' + str(duplicate_num) + '/'):
duplicate_num += 1
summary_dir += '_' + str(duplicate_num) + '/'
print 'Creating directory %s for logs.' % summary_dir
os.mkdir(summary_dir)
writer = tf.summary.FileWriter(summary_dir, sess.graph)
loader = Dataset2(train_data, train_label)
print 'Training data loaded.'
best_evil_accuracy = -1.0
best_res_epoch = -1
best_res = None
for epoch_num in range(opt.max_epoch):
print 'Epoch %d' % epoch_num
# Randomly shuffle the data.
random_indices = np.arange(train_data.shape[0])
np.random.shuffle(random_indices)
train_data = train_data[random_indices, :]
train_label = train_label[random_indices, :]
real_buckets = []
for lbl in range(train_label.shape[1]):
real_buckets.append(np.where(train_label[:, lbl] == 1)[0])
# Mini-batch Gradient Descent.
batch_no = 0
while (batch_no * batch_size) < train_data.shape[0]:
head = batch_no * batch_size
if head + batch_size <= train_data.shape[0]:
tail = head + batch_size
else:
tail = train_data.shape[0]
head = train_data.shape[0] - batch_size
feed_data = train_data[head:tail, :]
evil_labels = train_label[head:tail, :]
good_labels = odd_even_labels(evil_labels)
# Finding randomly sampled real data.
real_data = np.zeros_like(feed_data)
# Indices of training batch with specific label.
# label_indices[i] = indices of feed data, that have evil_label[i] == 1.
label_indices = [np.where(evil_labels[:, lbl] == 1)[0] \
for lbl in range(evil_labels.shape[1])]
for lbl in range(evil_labels.shape[1]):
# We take a random sample of size |label_indices[lbl]|
# from the real bucket of `lbl`.
selected_real_data = np.random.choice(
real_buckets[lbl], label_indices[lbl].shape[0])
# We put this random sample in the same index of their
# corresponding batch training data.
real_data[label_indices[lbl], :] = train_data[
selected_real_data, :]
feed = {
model.source: feed_data,
model.target: real_data,
model.good_labels: good_labels,
model.evil_labels: evil_labels
}
# Train G.
for _ in range(opt.g_train_num):
summary_str, G_loss, gan_loss, hinge_loss, l1_loss, l2_loss, \
good_fn_loss, evil_fn_loss, adv_loss, total_loss, _ = sess.run([
model.total_loss_merge_sum,
model.g_loss,
model.gan_loss,
model.hinge_loss,
model.l1_loss,
model.l2_loss,
model.good_fn_loss,
model.evil_fn_loss,
model.adv_loss,
model.total_loss,
model.G_train_op], feed)
writer.add_summary(summary_str, global_step)
# Train D.
for _ in range(opt.d_train_num):
summary_str, D_loss, _ = sess.run([
model.total_loss_merge_sum, model.d_loss,
model.D_pre_train_op
], feed)
writer.add_summary(summary_str, global_step)
global_step += 1
batch_no += 1
# Validation after each trainig epoch.
print '\tD: %.4f, G: %.4f\n\thinge(%.1f): %.4f, L1(%.1f): %.4f, L2(%.1f): %.4f' % (
D_loss, G_loss, opt.H_lambda, hinge_loss, opt.L1_lambda,
l1_loss, opt.L2_lambda, l2_loss)
print '\t\tGAN total loss: %.4f' % gan_loss
print '\tGood: %.4f, Evil: %.4f' % (good_fn_loss, evil_fn_loss)
print '\tAdv: %.4f, Total: %.4f' % (adv_loss, total_loss)
new_pred_data = []
head = 0
last_batch = False
while head < test_data.shape[0]:
if head + batch_size <= test_data.shape[0]:
tail = head + batch_size
else:
tail = test_data.shape[0]
head = test_data.shape[0] - batch_size
last_batch = True
if opt.cgan_gen:
cur_data = sess.run(
model.fake_images_sample,
{model.evil_labels: test_label[head:tail, :]})
else:
cur_data = sess.run(
model.fake_images_sample,
{model.source: test_data[head:tail, :]})
if last_batch:
new_pred_data.append(
cur_data[-(test_data.shape[0] % batch_size):, :])
else:
new_pred_data.append(cur_data)
head += batch_size
new_pred_data = np.concatenate(new_pred_data)
good_pred = np.argmax(
model.good_model.model.predict(new_pred_data), axis=1)
evil_pred = np.argmax(
model.evil_model.model.predict(new_pred_data), axis=1)
evil_true = np.argmax(test_label, axis=1)
good_true = np.argmax(odd_even_labels(test_label), axis=1)
good_accuracy = accuracy_score(good_true, good_pred)
evil_accuracy = accuracy_score(evil_true, evil_pred)
total_good_confusion = confusion_matrix(good_true, good_pred)
total_evil_confusion = confusion_matrix(evil_true,
evil_pred,
labels=range(
opt.evil_label_num))
print '\tGood Accuracy: %.4f, Evil Accuracy: %.4f' % (
good_accuracy, evil_accuracy)
print '\tAccuracy diff: %f' % (good_accuracy - evil_accuracy)
print 'Good confusion matrix:'
print total_good_confusion
print 'Evil confusion matrix:'
print total_evil_confusion
# Creating snapshots to save.
if opt.cgan_gen:
fake_samples = sess.run(model.fake_images_sample,
{model.evil_labels: output_labels})
else:
fake_samples, fake_noise = sess.run(
[model.fake_images_sample, model.sample_noise],
{model.source: output_samples})
max_accuracy_diff = good_accuracy - evil_accuracy
fakes = merge(fake_samples[:100, :], [10, 10])
separator = np.ones((280, 2))
original = merge(output_samples[:100].reshape(-1, 28, 28, 1),
[10, 10])
if opt.cgan_gen:
scipy.misc.imsave(
'snapshot_%d.png' % epoch_num,
np.concatenate([fakes, separator, original], axis=1))
else:
noise = merge(fake_noise[:100], [10, 10])
scipy.misc.imsave(
'snapshot_%d.png' % epoch_num,
np.concatenate([fakes, noise, original], axis=1))
# Only for the purpose of finding best D and G training times.
if evil_accuracy > best_evil_accuracy:
best_evil_accuracy = evil_accuracy
best_res_epoch = epoch_num
if opt.cgan_gen:
best_res = np.concatenate([fakes, separator, original],
axis=1)
else:
best_res = np.concatenate([fakes, noise, original], axis=1)
best_image_path = 'best_dn_%d_gn_%d_%d_epoch_%d.png' % \
(opt.d_train_num, opt.g_train_num, duplicate_num, best_res_epoch)
scipy.misc.imsave(best_image_path, best_res)
# print 'Maximum iterations: %d' % opt.max_iteration
# while iteration < opt.max_iteration:
# # this function returns (data, label, np.array(target)).
# # data = loader.next_batch(batch_size, negative=False)
# feed_data, evil_labels, real_data = loader.next_batch(
# batch_size, negative=False)
# good_labels = odd_even_labels(evil_labels)
# feed = {
# model.source: feed_data,
# model.target: real_data,
# model.good_labels: good_labels,
# model.evil_labels: evil_labels
# }
# # if opt.cgan_gen:
# # feed[model.label_clue] = label_clue[evil_labels.argmax(axis=1)]
# # Training G once.
# # summary_str, G_loss, _ = sess.run(
# # [model.total_loss_merge_sum, model.g_loss, model.G_train_op], feed)
# # writer.add_summary(summary_str, iteration)
# # Training G twice.
# summary_str, G_loss, gan_loss, hinge_loss, l1_loss, l2_loss, \
# good_fn_loss, evil_fn_loss, adv_loss, total_loss, _ = sess.run([
# model.total_loss_merge_sum,
# model.g_loss,
# model.gan_loss,
# model.hinge_loss,
# model.l1_loss,
# model.l2_loss,
# model.good_fn_loss,
# model.evil_fn_loss,
# model.adv_loss,
# model.total_loss,
# model.G_train_op], feed)
# writer.add_summary(summary_str, iteration)
# # Training D.
# for _ in range(opt.d_train_num):
# summary_str, D_loss, _ = sess.run(
# [model.total_loss_merge_sum, model.d_loss, model.D_pre_train_op], feed)
# writer.add_summary(summary_str, iteration)
# if iteration % opt.losses_log_every == 0:
# # if iteration != 0 and iteration % opt.save_checkpoint_every == 0:
# # checkpoint_path = os.path.join(opt.checkpoint_path, 'checkpoint.ckpt')
# # print 'Saving the model in "%s"' % checkpoint_path
# # model.saver.save(sess, checkpoint_path, global_step=iteration)
# # test_loader = Dataset2(test_data, test_label)
# # test_num = test_loader._num_examples
# # test_iter_num = (test_num - batch_size) / batch_size
# # total_evil_accuracy = 0.0
# # total_good_accuracy = 0.0
# # fake_samples = [[] for _ in range(test_loader._num_labels)]
# # fake_noise = [[] for _ in range(test_loader._num_labels)]
# # original_samples = [[] for _ in range(test_loader._num_labels)]
# # for _ in range(test_iter_num):
# # # Loading the next batch of test images
# # test_input_data, test_evil_labels, _ = \
# # test_loader.next_batch(batch_size)
# # evil_categorical_labels = np.argmax(test_evil_labels, axis=1)
# # test_good_labels = odd_even_labels(test_evil_labels)
# # feed = {
# # model.source: test_input_data,
# # model.evil_labels: test_evil_labels,
# # model.good_labels: test_good_labels
# # }
# # # if opt.cgan_gen:
# # # feed[model.label_clue] = label_clue[test_evil_labels.argmax(axis=1)]
# # evil_accuracy, good_accuracy = sess.run(
# # [model.evil_accuracy, model.good_accuracy], feed)
# # # We divide the total accuracy by the number of test iterations.
# # total_good_accuracy += good_accuracy
# # total_evil_accuracy += evil_accuracy
# # # print 'Evil accuracy: %.6f\tGood accuracy: %.6f' % (
# # # evil_accuracy, good_accuracy)
# # # test_accuracy, test_adv_accuracy = sess.run(
# # # [model.accuracy, model.adv_accuracy], feed)
# # # test_acc += test_accuracy
# # # test_adv_acc += test_adv_accuracy
# # # fake_images, g_x = sess.run(
# # # [model.fake_images_sample, model.sample_noise],
# # # {model.source: test_input_data})
# # # for lbl in range(test_loader._num_labels):
# # # if len(fake_samples[lbl]) < 10:
# # # idx = np.where(evil_categorical_labels == lbl)[0]
# # # if idx.shape[0] >= 10:
# # # fake_samples[lbl] = fake_images[idx[:10]]
# # # fake_noise[lbl] = g_x[idx[:10]]
# # # original_samples[lbl] = test_input_data[idx[:10]]
# # # for lbl, sample, noise in zip(test_evil_labels, fake_images, fake_noise):
# # # if len(fake_samples[lbl]) > 10:
# # # continue
# # # fake_samples[lbl].append(sample)
# # # fake_noise[lbl].append(noise)
# # # pdb.set_trace()
# # # print fake_images.shape
# # # Finding those predicted labels that are equal to the target label
# # # idxs = np.where(out_predict_labels == target_label)[0]
# # # save_images(samples[:100], [10, 10], 'CIFAR10/result2/test_' + str(source_idx) + str(target_idx)+ '_.png')
# # # pdb.set_trace()
# # # show_samples.append(samples)
# # # input_samples.append(s_imgs)
# # # save_samples.append(samples)
# # # if opt.is_advGAN:
# # # save_samples.append(samples[idxs])
# # # else:
# # # We add all samples.
# # # show_samples = np.concatenate(show_samples, axis=0)
# # # save_samples = np.concatenate(save_samples, axis=0)
# # good_accuracy = total_good_accuracy / float(test_iter_num)
# # evil_accuracy = total_evil_accuracy / float(test_iter_num)
# # print '\tAccuracy diff: %f' % (good_accuracy - evil_accuracy)
# # print '\tGood accuracy %f, Evil accuracy %f' % (
# # good_accuracy, evil_accuracy)
# # Resizing the samples to save them later on.
# # fake_samples = np.reshape(np.array(fake_samples), [100, -1])
# # original_samples = np.reshape(np.array(original_samples), [100, -1])
# # fake_noise = np.reshape(np.array(fake_noise), [100, -1])
# # if (good_accuracy - evil_accuracy) > max_accuracy_diff:
# # test_accuracy = test_acc / float(test_iter_num)
# # test_adv_accuracy = test_adv_acc / float(test_iter_num)
# # if (good_accuracy - evil_accuracy) > max_accuracy_diff:
# # max_accuracy_diff = good_accuracy - evil_accuracy
# # if min_adv_accuracy > test_adv_accuracy:
# # min_adv_accuracy = test_adv_accuracy
# # save_images(fake_images[:100], [10, 10], 'fake.png')
# # save_images(test_input_data[:100], [10, 10], 'real.png')
# # all_idx = np.arange(100)
# # odds = np.where((all_idx / 10) % 2 == 1)[0]
# # evens = np.where((all_idx / 10) % 2 == 0)[0]
# # order = np.concatenate((odds, evens))
# # save_images(fake_samples[order], [10, 10], 'best_images.png')
# # save_images(fake_noise[order], [10, 10], 'best_noise.png')
# # save_images(original_samples[order], [10, 10], 'best_original.png')
# # save_anything = True
# # Saving the best yet model.
# # best_model_path = os.path.join(opt.checkpoint_path, 'best.ckpt')
# # print 'Saving the best model yet at "%s"' % best_model_path
# # model.saver.save(sess, best_model_path)
# # if save_anything is False:
# # # Nothing is saved. We save a version here.
# # save_images(fake_samples[:100], [10, 10], 'last_images.png')
# # save_images(fake_noise[:100], [10, 10], 'last_noise.png')
# # save_anything = True
# iteration += 1
# We can transform the training and test data given in the beginning here.
# This is only half the actual data.
if opt.save_data:
# if opt.cgan_gen:
raise NotImplementedError(
'Saving data for CGAN_GEN is not yet implemented.')
def main(args):
with tf.Session() as sess:
if (args['dataset'] == 'mnist'):
data, model = MNIST(), MNISTModel("models/mnist", sess)
handpick = False
inception = False
if (args['dataset'] == "cifar"):
data, model = CIFAR(), CIFARModel("models/cifar", sess)
handpick = True
inception = False
if (args['dataset'] == "imagenet"):
data, model = ImageNet(args['seed_imagenet']), InceptionModel(sess)
handpick = True
inception = True
if (args['adversarial'] != "none"):
model = MNISTModel("models/mnist_cw" + str(args['adversarial']),
sess)
if (args['temp'] and args['dataset'] == 'mnist'):
model = MNISTModel("models/mnist-distilled-" + str(args['temp']),
sess)
if (args['temp'] and args['dataset'] == 'cifar'):
model = CIFARModel("models/cifar-distilled-" + str(args['temp']),
sess)
inputs, targets, labels, true_ids = generate_data(
data,
model,
samples=args['numimg'],
inception=inception,
handpick=handpick,
train=args['train'],
seed=args['seed'])
timestart = time.time()
if (args['attack'] == 'L2'):
attack = CarliniL2(sess,
model,
batch_size=args['batch_size'],
max_iterations=args['maxiter'],
confidence=args['conf'],
binary_search_steps=args['binary_steps'],
beta=args['beta'],
abort_early=args['abort_early'])
adv = attack.attack(inputs, targets)
if (args['attack'] == 'L1'):
attack = EADL1(sess,
model,
batch_size=args['batch_size'],
max_iterations=args['maxiter'],
confidence=args['conf'],
binary_search_steps=args['binary_steps'],
beta=args['beta'],
abort_early=args['abort_early'])
adv = attack.attack(inputs, targets)
if (args['attack'] == 'EN'):
attack = EADEN(sess,
model,
batch_size=args['batch_size'],
max_iterations=args['maxiter'],
confidence=args['conf'],
binary_search_steps=args['binary_steps'],
beta=args['beta'],
abort_early=args['abort_early'])
adv = attack.attack(inputs, targets)
"""If untargeted, pass labels instead of targets"""
if (args['attack'] == 'FGSM'):
attack = FGM(sess,
model,
batch_size=args['batch_size'],
ord=np.inf,
inception=inception)
adv = attack.attack(inputs, targets)
if (args['attack'] == 'FGML1'):
attack = FGM(sess,
model,
batch_size=args['batch_size'],
ord=1,
inception=inception)
adv = attack.attack(inputs, targets)
if (args['attack'] == 'FGML2'):
attack = FGM(sess,
model,
batch_size=args['batch_size'],
ord=2,
inception=inception)
adv = attack.attack(inputs, targets)
if (args['attack'] == 'IFGSM'):
attack = IGM(sess,
model,
batch_size=args['batch_size'],
ord=np.inf,
inception=inception)
adv = attack.attack(inputs, targets)
if (args['attack'] == 'IFGML1'):
attack = IGM(sess,
model,
batch_size=args['batch_size'],
ord=1,
inception=inception)
adv = attack.attack(inputs, targets)
if (args['attack'] == 'IFGML2'):
attack = IGM(sess,
model,
batch_size=args['batch_size'],
ord=2,
inception=inception)
adv = attack.attack(inputs, targets)
timeend = time.time()
print("Took", timeend - timestart, "seconds to run",
len(inputs) / args['batch_size'], "random instances.")
if (args['train']):
np.save('labels_train.npy', labels)
np.save(str(args['attack']) + '_train.npy', adv)
return
r_best = []
d_best_l1 = []
d_best_l2 = []
d_best_linf = []
r_average = []
d_average_l1 = []
d_average_l2 = []
d_average_linf = []
r_worst = []
d_worst_l1 = []
d_worst_l2 = []
d_worst_linf = []
if (args['conf'] != 0):
model = MNISTModel("models/mnist-distilled-100", sess)
if (args['show']):
if not os.path.exists(
str(args['save']) + "/" + str(args['dataset']) + "/" +
str(args['attack'])):
os.makedirs(
str(args['save']) + "/" + str(args['dataset']) + "/" +
str(args['attack']))
for i in range(0, len(inputs), args['batch_size']):
pred = []
for j in range(i, i + args['batch_size']):
if inception:
pred.append(
np.reshape(model.model.predict(adv[j:j + 1]),
(data.test_labels[0:1].shape)))
else:
pred.append(model.model.predict(adv[j:j + 1]))
dist_l1 = 1e10
dist_l2 = 1e10
dist_linf = 1e10
dist_l1_index = 1e10
dist_l2_index = 1e10
dist_linf_index = 1e10
for k, j in enumerate(range(i, i + args['batch_size'])):
if (np.argmax(pred[k], 1) == np.argmax(targets[j:j + 1], 1)):
if (np.sum(np.abs(adv[j] - inputs[j])) < dist_l1):
dist_l1 = np.sum(np.abs(adv[j] - inputs[j]))
dist_l1_index = j
if (np.amax(np.abs(adv[j] - inputs[j])) < dist_linf):
dist_linf = np.amax(np.abs(adv[j] - inputs[j]))
dist_linf_index = j
if ((np.sum((adv[j] - inputs[j])**2)**.5) < dist_l2):
dist_l2 = (np.sum((adv[j] - inputs[j])**2)**.5)
dist_l2_index = j
if (dist_l1_index != 1e10):
d_best_l2.append((np.sum(
(adv[dist_l2_index] - inputs[dist_l2_index])**2)**.5))
d_best_l1.append(
np.sum(np.abs(adv[dist_l1_index] - inputs[dist_l1_index])))
d_best_linf.append(
np.amax(
np.abs(adv[dist_linf_index] -
inputs[dist_linf_index])))
r_best.append(1)
else:
r_best.append(0)
rand_int = np.random.randint(i, i + args['batch_size'])
if inception:
pred_r = np.reshape(
model.model.predict(adv[rand_int:rand_int + 1]),
(data.test_labels[0:1].shape))
else:
pred_r = model.model.predict(adv[rand_int:rand_int + 1])
if (np.argmax(pred_r,
1) == np.argmax(targets[rand_int:rand_int + 1], 1)):
r_average.append(1)
d_average_l2.append(
np.sum((adv[rand_int] - inputs[rand_int])**2)**.5)
d_average_l1.append(
np.sum(np.abs(adv[rand_int] - inputs[rand_int])))
d_average_linf.append(
np.amax(np.abs(adv[rand_int] - inputs[rand_int])))
else:
r_average.append(0)
dist_l1 = 0
dist_l1_index = 1e10
dist_linf = 0
dist_linf_index = 1e10
dist_l2 = 0
dist_l2_index = 1e10
for k, j in enumerate(range(i, i + args['batch_size'])):
if (np.argmax(pred[k], 1) != np.argmax(targets[j:j + 1], 1)):
r_worst.append(0)
dist_l1_index = 1e10
dist_l2_index = 1e10
dist_linf_index = 1e10
break
else:
if (np.sum(np.abs(adv[j] - inputs[j])) > dist_l1):
dist_l1 = np.sum(np.abs(adv[j] - inputs[j]))
dist_l1_index = j
if (np.amax(np.abs(adv[j] - inputs[j])) > dist_linf):
dist_linf = np.amax(np.abs(adv[j] - inputs[j]))
dist_linf_index = j
if ((np.sum((adv[j] - inputs[j])**2)**.5) > dist_l2):
dist_l2 = (np.sum((adv[j] - inputs[j])**2)**.5)
dist_l2_index = j
if (dist_l1_index != 1e10):
d_worst_l2.append((np.sum(
(adv[dist_l2_index] - inputs[dist_l2_index])**2)**.5))
d_worst_l1.append(
np.sum(np.abs(adv[dist_l1_index] - inputs[dist_l1_index])))
d_worst_linf.append(
np.amax(
np.abs(adv[dist_linf_index] -
inputs[dist_linf_index])))
r_worst.append(1)
if (args['show']):
for j in range(i, i + args['batch_size']):
target_id = np.argmax(targets[j:j + 1], 1)
label_id = np.argmax(labels[j:j + 1], 1)
prev_id = np.argmax(
np.reshape(model.model.predict(inputs[j:j + 1]),
(data.test_labels[0:1].shape)), 1)
adv_id = np.argmax(
np.reshape(model.model.predict(adv[j:j + 1]),
(data.test_labels[0:1].shape)), 1)
suffix = "id{}_seq{}_lbl{}_prev{}_adv{}_{}_l1_{:.3f}_l2_{:.3f}_linf_{:.3f}".format(
true_ids[i], target_id, label_id, prev_id,
adv_id, adv_id == target_id,
np.sum(np.abs(adv[j] - inputs[j])),
np.sum((adv[j] - inputs[j])**2)**.5,
np.amax(np.abs(adv[j] - inputs[j])))
show(
inputs[j:j + 1],
str(args['save']) + "/" + str(args['dataset']) + "/" +
str(args['attack']) +
"/original_{}.png".format(suffix))
show(
adv[j:j + 1],
str(args['save']) + "/" + str(args['dataset']) + "/" +
str(args['attack']) +
"/adversarial_{}.png".format(suffix))
print('best_case_L1_mean', np.mean(d_best_l1))
print('best_case_L2_mean', np.mean(d_best_l2))
print('best_case_Linf_mean', np.mean(d_best_linf))
print('best_case_prob', np.mean(r_best))
print('average_case_L1_mean', np.mean(d_average_l1))
print('average_case_L2_mean', np.mean(d_average_l2))
print('average_case_Linf_mean', np.mean(d_average_linf))
print('average_case_prob', np.mean(r_average))
print('worst_case_L1_mean', np.mean(d_worst_l1))
print('worst_case_L2_mean', np.mean(d_worst_l2))
print('worst_case_Linf_mean', np.mean(d_worst_linf))
print('worst_case_prob', np.mean(r_worst))
def load_model(self,
dataset="mnist",
model_name="2-layer",
activation="relu",
model=None,
batch_size=0,
compute_slope=False,
order=1):
"""
model: if set to None, then load dataset with model_name. Otherwise use the model directly.
dataset: mnist, cifar and imagenet. recommend to use mnist and cifar as a starting point.
model_name: possible options are 2-layer, distilled, and normal
"""
from setup_cifar import CIFAR, CIFARModel, TwoLayerCIFARModel
from setup_mnist import MNIST, MNISTModel, TwoLayerMNISTModel
from nlayer_model import NLayerModel
from setup_imagenet import ImageNet, ImageNetModel
# if set this to true, we will use the logit layer output instead of probability
# the logit layer's gradients are usually larger and more stable
output_logits = True
self.dataset = dataset
self.model_name = model_name
if model is None:
print('Loading model...')
if dataset == "mnist":
self.batch_size = 1024
if model_name == "2-layer":
model = TwoLayerMNISTModel("models/mnist_2layer",
self.sess, not output_logits)
elif model_name == "normal":
if activation == "relu":
model = MNISTModel("models/mnist", self.sess,
not output_logits)
else:
print("actviation = {}".format(activation))
model = MNISTModel("models/mnist_cnn_7layer_" +
activation,
self.sess,
not output_logits,
activation=activation)
time.sleep(5)
elif model_name == "brelu":
model = MNISTModel("models/mnist_brelu",
self.sess,
not output_logits,
use_brelu=True)
elif model_name == "distilled":
model = MNISTModel("models/mnist-distilled-100", self.sess,
not output_logits)
else:
# specify model parameters as N,M,opts
model_params = model_name.split(",")
if len(model_params) < 3:
raise (RuntimeError("incorrect model option" +
model_name))
numlayer = int(model_params[0])
nhidden = int(model_params[1])
modelfile = "models/mnist_{}layer_relu_{}_{}".format(
numlayer, nhidden, model_params[2])
print("loading", modelfile)
model = NLayerModel([nhidden] * (numlayer - 1), modelfile)
elif dataset == "cifar":
self.batch_size = 1024
if model_name == "2-layer":
model = TwoLayerCIFARModel("models/cifar_2layer",
self.sess, not output_logits)
elif model_name == "normal":
if activation == "relu":
model = CIFARModel("models/cifar", self.sess,
not output_logits)
else:
model = CIFARModel("models/cifar_cnn_7layer_" +
activation,
self.sess,
not output_logits,
activation=activation)
elif model_name == "brelu":
model = CIFARModel("models/cifar_brelu",
self.sess,
not output_logits,
use_brelu=True)
elif model_name == "distilled":
model = CIFARModel("models/cifar-distilled-100", self.sess,
not output_logits)
else:
# specify model parameters as N,M,opts
model_params = model_name.split(",")
if len(model_params) < 3:
raise (RuntimeError("incorrect model option" +
model_name))
numlayer = int(model_params[0])
nhidden = int(model_params[1])
modelfile = "models/cifar_{}layer_relu_{}_{}".format(
numlayer, nhidden, model_params[2])
print("loading", modelfile)
model = NLayerModel([nhidden] * (numlayer - 1),
modelfile,
image_size=32,
image_channel=3)
elif dataset == "imagenet":
self.batch_size = 32
model = ImageNetModel(self.sess,
use_softmax=not output_logits,
model_name=model_name,
create_prediction=False)
else:
raise (RuntimeError("dataset unknown"))
#print("*** Loaded model successfully")
self.model = model
self.compute_slope = compute_slope
if batch_size != 0:
self.batch_size = batch_size
## placeholders: self.img, self.true_label, self.target_label
# img is the placeholder for image input
self.img = tf.placeholder(shape=[
None, model.image_size, model.image_size, model.num_channels
],
dtype=tf.float32)
# output is the output tensor of the entire network
self.output = model.predict(self.img)
# create the graph to compute gradient
# get the desired true label and target label
self.true_label = tf.placeholder(dtype=tf.int32, shape=[])
self.target_label = tf.placeholder(dtype=tf.int32, shape=[])
true_output = self.output[:, self.true_label]
target_output = self.output[:, self.target_label]
# get the difference
self.objective = true_output - target_output
# get the gradient(deprecated arguments)
self.grad_op = tf.gradients(self.objective, self.img)[0]
# compute gradient norm: (in computation graph, so is faster)
grad_op_rs = tf.reshape(self.grad_op, (tf.shape(self.grad_op)[0], -1))
self.grad_2_norm_op = tf.norm(grad_op_rs, axis=1)
self.grad_1_norm_op = tf.norm(grad_op_rs, ord=1, axis=1)
self.grad_inf_norm_op = tf.norm(grad_op_rs, ord=np.inf, axis=1)
### Lily: added Hessian-vector product calculation here for 2nd order bound:
if order == 2:
## _hessian_vector_product(ys, xs, v): return a list of tensors containing the product between the Hessian and v
## ys: a scalar valur or a tensor or a list of tensors to be summed to yield of scalar
## xs: a list of tensors that we should construct the Hessian over
## v: a list of tensors with the same shape as xs that we want to multiply by the Hessian
# self.randv: shape = (Nimg,28,28,1) (the v in _hessian_vector_product)
self.randv = tf.placeholder(shape=[
None, model.image_size, model.image_size, model.num_channels
],
dtype=tf.float32)
# hv_op_tmp: shape = (Nimg,28,28,1) for mnist, same as self.img (the xs in _hessian_vector_product)
hv_op_tmp = gradients_impl._hessian_vector_product(
self.objective, [self.img], [self.randv])[0]
# hv_op_rs: reshape hv_op_tmp to hv_op_rs whose shape = (Nimg, 784) for mnist
hv_op_rs = tf.reshape(hv_op_tmp, (tf.shape(hv_op_tmp)[0], -1))
# self.hv_norm_op: norm of hessian vector product, keep shape = (Nimg,1) using keepdims
self.hv_norm_op = tf.norm(hv_op_rs, axis=1, keepdims=True)
# hv_op_rs_normalize: normalize Hv to Hv/||Hv||, shape = (Nimg, 784)
hv_op_rs_normalize = hv_op_rs / self.hv_norm_op
# self.hv_op: reshape hv_op_rs_normalize to shape = (Nimg,28,28,1)
self.hv_op = tf.reshape(hv_op_rs_normalize, tf.shape(hv_op_tmp))
## reshape randv and compute its norm
# shape: (Nimg, 784)
randv_rs = tf.reshape(self.randv, (tf.shape(self.randv)[0], -1))
# shape: (Nimg,)
self.randv_norm_op = tf.norm(randv_rs, axis=1)
## compute v'Hv: use un-normalized Hv (hv_op_tmp, hv_op_rs)
# element-wise multiplication and then sum over axis = 1 (now shape: (Nimg,))
self.vhv_op = tf.reduce_sum(tf.multiply(randv_rs, hv_op_rs),
axis=1)
## compute Rayleigh quotient: v'Hv/v'v (estimated largest eigenvalue), shape: (Nimg,)
# note: self.vhv_op and self.randv_norm_op has to be in the same dimension (either (Nimg,) or (Nimg,1))
self.eig_est = self.vhv_op / tf.square(self.randv_norm_op)
## Lily added the tf.while to compute the eigenvalue in computational graph later
# cond for computing largest abs/neg eigen-value
def cond(it, randv, eig_est, eig_est_prev, tfconst):
norm_diff = tf.norm(eig_est - eig_est_prev, axis=0)
return tf.logical_and(it < 500, norm_diff > 0.001)
# compute largest abs eigenvalue: tfconst = 0
# compute largest neg eigenvalue: tfconst = 10
def body(it, randv, eig_est, eig_est_prev, tfconst):
#hv_op_tmp = gradients_impl._hessian_vector_product(self.objective, [self.img], [randv])[0]-10*randv
hv_op_tmp = gradients_impl._hessian_vector_product(
self.objective, [self.img], [randv])[0] - tf.multiply(
tfconst, randv)
hv_op_rs = tf.reshape(hv_op_tmp, (tf.shape(hv_op_tmp)[0], -1))
hv_norm_op = tf.norm(hv_op_rs, axis=1, keepdims=True)
hv_op_rs_normalize = hv_op_rs / hv_norm_op
hv_op = tf.reshape(hv_op_rs_normalize, tf.shape(hv_op_tmp))
randv_rs = tf.reshape(randv, (tf.shape(randv)[0], -1))
randv_norm_op = tf.norm(randv_rs, axis=1)
vhv_op = tf.reduce_sum(tf.multiply(randv_rs, hv_op_rs), axis=1)
eig_est_prev = eig_est
eig_est = vhv_op / tf.square(randv_norm_op)
return (it + 1, hv_op, eig_est, eig_est_prev, tfconst)
it = tf.constant(0)
# compute largest abs eigenvalue
result = tf.while_loop(
cond, body,
[it, self.randv, self.vhv_op, self.eig_est,
tf.constant(0.0)])
# compute largest neg eigenvalue
self.shiftconst = tf.placeholder(shape=(), dtype=tf.float32)
result_1 = tf.while_loop(
cond, body,
[it, self.randv, self.vhv_op, self.eig_est, self.shiftconst])
# computing largest abs eig value and save result
self.it = result[0]
self.while_hv_op = result[1]
self.while_eig = result[2]
# computing largest neg eig value and save result
self.it_1 = result_1[0]
#self.while_eig_1 = tf.add(result_1[2], tfconst)
self.while_eig_1 = tf.add(result_1[2], result_1[4])
show_tensor_op = False
if show_tensor_op:
print("====================")
print("Define hessian_vector_product operator: ")
print("hv_op_tmp = {}".format(hv_op_tmp))
print("hv_op_rs = {}".format(hv_op_rs))
print("self.hv_norm_op = {}".format(self.hv_norm_op))
print("hv_op_rs_normalize = {}".format(hv_op_rs_normalize))
print("self.hv_op = {}".format(self.hv_op))
print("self.grad_op = {}".format(self.grad_op))
print("randv_rs = {}".format(randv_rs))
print("self.randv_norm_op = {}".format(self.randv_norm_op))
print("self.vhv_op = {}".format(self.vhv_op))
print("self.eig_est = {}".format(self.eig_est))
print("====================")
return self.img, self.output
def main(args):
with tf.Session() as sess:
if args['dataset'] == 'mnist':
data, model = MNIST(), MNISTModel("models/mnist", sess)
handpick = False
inception = False
if args['dataset'] == "cifar":
data, model = CIFAR(), CIFARModel("models/cifar", sess)
handpick = True
inception = False
if args['dataset'] == "imagenet":
data, model = ImageNet(args['seed_imagenet']), InceptionModel(sess)
handpick = True
inception = True
if args['adversarial'] != "none":
model = MNISTModel("models/mnist_cwl2_admm" + str(args['adversarial']), sess)
if args['temp'] and args['dataset'] == 'mnist':
model = MNISTModel("models/mnist-distilled-" + str(args['temp']), sess)
if args['temp'] and args['dataset'] == 'cifar':
model = CIFARModel("models/cifar-distilled-" + str(args['temp']), sess)
inputs, targets, labels, true_ids = generate_data_ST(data, model, samples=args['numimg'],
samplesT=args['numimgT'], targeted=True,
start=0, inception=inception, handpick=handpick, seed=args['seed'])
#print(true_ids)
if args['attack'] == 'L2C':
attack = CarliniL2(sess, model, batch_size=args['batch_size'], max_iterations=args['maxiter'],
confidence=args['conf'],
binary_search_steps=args['binary_steps'],
abort_early=args['abort_early'])
if args['attack'] == 'L2LA2':
attack = LADMML2re(sess, model, batch_size=args['batch_size'], max_iterations=args['maxiter'],
layernum=args['layer_number'], use_kernel=args['use_kernel'],
confidence=args['conf'], binary_search_steps=args['iteration_steps'], ro=args['ro'],
abort_early=args['abort_early'])
timestart = time.time()
adv = attack.attack(inputs, targets)
timeend = time.time()
print("Took", timeend - timestart, "seconds to run", len(inputs), "samples.\n")
if args['conf'] != 0:
model = MNISTModel("models/mnist-distilled-100", sess)
if args['kernel_bias']:
EP = evaluate_perturbation_kb(args, sess, model, inputs)
scores, l2 = EP(inputs, targets, adv)
EPT = evaluate_perturbation_testset(args, sess, model, data.test_data)
test_scores = EPT(data.test_data, data.test_labels)
EP2 = evaluate_perturbation_kb_restore(args, sess, model, inputs)
scores2 = EP2(inputs, targets, adv)
EPT2 = evaluate_perturbation_testset(args, sess, model, data.test_data)
test_scores2 = EPT2(data.test_data, data.test_labels)
else:
EP = evaluate_perturbation(args, sess, model, inputs)
# scores = EP(inputs, targets, adv)
# scores2 = EP2(inputs, targets, adv)
score_count = []
score_count2 = []
score_count3 = []
score_count4 = []
for e, (sc) in enumerate(scores):
if np.argmax(sc) == np.argmax(targets[e]):
score_count.append(1)
if e < args['numimg']:
score_count4.append(1)
else:
score_count.append(0)
if e < args['numimg']:
score_count4.append(0)
for e, (sc) in enumerate(scores):
if np.argmax(sc) == np.argmax(labels[e]):
score_count3.append(1)
else:
score_count3.append(0)
for e, (sc2) in enumerate(scores2):
if np.argmax(sc2) == np.argmax(labels[e]):
score_count2.append(1)
else:
score_count2.append(0)
test_score_count = []
test_score_count2 = []
for e, (tsc) in enumerate(test_scores):
if np.argmax(tsc) == np.argmax(data.test_labels[e]):
test_score_count.append(1)
else:
test_score_count.append(0)
for e, (tsc2) in enumerate(test_scores2):
if np.argmax(tsc2) == np.argmax(data.test_labels[e]):
test_score_count2.append(1)
else:
test_score_count2.append(0)
l0s = np.count_nonzero(adv)
successrate = np.mean(score_count)
successrate2 = np.mean(score_count2)
successrate3 = np.mean(score_count3)
test_successrate = np.mean(test_score_count)
test_successrate2 = np.mean(test_score_count2)
print('original model, success rate of T images for the original labels:', successrate2)
print('modified model, success rate of T images for the original labels:', successrate3)
print('modified model, success rate of T images for the target labels:', successrate)
print('modified model, success rate of S imges for the target labels:', np.mean(score_count4))
print('modified model, success rate of test set for the original labels:', test_successrate)
print('original model, success rate of test set for the original labels:', test_successrate2)
print('l0 distance:', l0s)
print('l2 distance:', l2)
K = int(sys.argv[3])
bias = float(sys.argv[4])
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
set_session(tf.Session(config=config))
sess = Keras.get_session()
Keras.set_learning_phase(False)
np.random.seed(1)
tf.set_random_seed(1)
if dataset == "MNIST":
data = MNIST()
model = MNISTModel("../1-Models/MNIST")
x = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
elif dataset == "CIFAR":
data = CIFAR()
model = CIFARModel("../1-Models/CIFAR")
x = tf.placeholder(tf.float32, shape=(None, 32, 32, 3))
training_accuracy = np.mean(np.argmax(model.model.predict(data.train_data), axis = 1) == np.argmax(data.train_labels, axis = 1))
print("Training Accuracy: " + str(training_accuracy))
testing_accuracy = np.mean(np.argmax(model.model.predict(data.test_data), axis = 1) == np.argmax(data.test_labels, axis = 1))
print("Testing Accuracy: " + str(testing_accuracy))
X = data.train_data
X_adv = np.load("../2-AEs/" + dataset + "/train_" + mode + ".npy")
pred_original = model.model.predict(X)
def main(args):
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.7)
with tf.Session() as sess:
if args['dataset'] == 'mnist':
data, model = MNIST(), MNISTModel("models/mnist", sess)
handpick = False
inception = False
if args['dataset'] == "cifar":
data, model = CIFAR(), CIFARModel("models/cifar", sess)
handpick = True
inception = False
if args['dataset'] == "imagenet":
data, model = ImageNet(args['seed_imagenet']), InceptionModel(sess)
handpick = True
inception = True
if args['adversarial'] != "none":
model = MNISTModel(
"models/mnist_cwl2_admm" + str(args['adversarial']), sess)
if args['temp'] and args['dataset'] == 'mnist':
model = MNISTModel("models/mnist-distilled-" + str(args['temp']),
sess)
if args['temp'] and args['dataset'] == 'cifar':
model = CIFARModel("models/cifar-distilled-" + str(args['temp']),
sess)
inputs, targets, labels, true_ids = generate_data(
data,
model,
samples=args['numimg'],
targeted=args['targeted'],
start=0,
inception=inception,
handpick=handpick,
seed=args['seed'])
#print(true_ids)
if args['attack'] == 'L2C':
attack = CarliniL2(sess,
model,
batch_size=args['batch_size'],
max_iterations=args['maxiter'],
confidence=args['conf'],
targeted=args['targeted'],
binary_search_steps=args['binary_steps'],
abort_early=args['abort_early'])
if args['attack'] == 'L2BB':
# score-based ZO-ADMM attack
attack = LADMMBB(sess,
model,
batch_size=args['batch_size'],
max_iterations=args['maxiter'],
targeted=args['targeted'],
confidence=args['conf'],
binary_search_steps=args['iteration_steps'],
ro=args['ro'],
abort_early=args['abort_early'],
gama=args['gama'],
epi=args['epi'],
alpha=args['alpha'])
timestart = time.time()
# adv = attack.attack(inputs, targets)
adv, querycount, queryl2 = attack.attack(inputs, targets)
timeend = time.time()
print("Took", timeend - timestart, "seconds to run", len(inputs),
"samples.\n")
if args['train']:
np.save('labels_train.npy', labels)
np.save(str(args['attack']) + '_train.npy', adv)
if (args['conf'] != 0):
model = MNISTModel("models/mnist-distilled-100", sess)
if args['targeted']:
l1_l2_li_computation(args, data, model, adv, inception, inputs,
targets, labels, true_ids, querycount,
queryl2)
else:
l2_computation(args, data, model, adv, inception, inputs, targets,
labels, true_ids, querycount, queryl2)
assert args.lsa ^ args.dsa, "Select either 'lsa' or 'dsa'"
print(args)
if args.d == "mnist":
#(x_train, y_train), (x_test, y_test) = mnist.load_data()
data = MNIST()
x_train = data.train_data
y_train = data.train_labels
x_test = data.test_data
y_test = data.test_labels
x_train = x_train.reshape(-1, 28, 28, 1)
x_test = x_test.reshape(-1, 28, 28, 1)
# Load pre-trained model.
#model = load_model("./model/model_mnist.h5")
model = MNISTModel("./models/mnist")
model = model.model
model.summary()
# You can select some layers you want to test.
# layer_names = ["activation_1"]
# layer_names = ["activation_2"]
layer_names = ["activation_3"]
# Load target set.
#x_target = np.load("./adv/adv_mnist_{}.npy".format(args.target))
#x_target = []
#for i in range(1, 10):
# target_img = imread("/tmp/adv_result_{}_to_0.jpg".format(i))
# x_target.append(target_img)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--model_path',
type=str,
default="models/",
help="Path to save trained model.")
parser.add_argument(
'--pert_data',
type=str,
default='./MNIST_data/perturbed.npz',
help='Path to LFW perturbed data.')
parser.add_argument(
'--orig_data',
type=str,
default='MNIST_data/B.npz',
help="Path to original data."
)
parser.add_argument(
'--image_size', type=int, default=28, help='Size of input images.')
parser.add_argument(
'--num_channels',
type=int,
default=1,
help='Number of channels in input images.')
parser.add_argument(
'--train_new',
dest='train_new',
action='store_true',
help='Train a new classifier.')
parser.set_defaults(train_new=False)
args = parser.parse_args()
loaded = np.load(args.pert_data)
pert_data = np.concatenate((loaded['train_data'], loaded['test_data']))
pert_data = pert_data.reshape(pert_data.shape[0], args.image_size, args.image_size, -1)
pert_evil_label = np.concatenate((loaded['train_label'], loaded['test_label']))
pert_good_label = odd_even_labels(pert_evil_label).\
argmax(axis=1)
pert_evil_label = np.argmax(pert_evil_label, axis=1)
loaded = np.load(args.orig_data)
orig_data = np.concatenate((loaded['train_data'], loaded['test_data']))
orig_data = orig_data.reshape(orig_data.shape[0], args.image_size, args.image_size, -1)
orig_evil_label = np.concatenate((loaded['train_label'], loaded['test_label']))
orig_good_label = odd_even_labels(orig_evil_label).\
argmax(axis=1)
orig_evil_label = np.argmax(orig_evil_label, axis=1)
print 'Original data shape:', orig_data.shape
good_used = OddEvenMNIST(args.model_path + 'A_odd_even')
good_left = OddEvenMNIST(args.model_path + 'C_odd_even')
evil_used = MNISTModel(args.model_path + 'A_digits')
evil_left = MNISTModel(args.model_path + 'C_digits')
evil_pair = (orig_evil_label, pert_evil_label)
good_pair = (orig_good_label, pert_good_label)
for model, label_pair, name in zip(
[evil_used, good_used, evil_left, good_left],
[evil_pair, good_pair, evil_pair, good_pair],
['Used Evil', 'Used Good', 'Left-out Evil', 'Left-out Good']):
org_true, pert_true = label_pair
print name + ':'
org_pred = np.argmax(model.model.predict(orig_data), axis=1)
print org_pred.shape
print org_true.shape
org_acc = accuracy_score(org_true, org_pred)
print '\tOriginal Accuracy: %.4f' % org_acc
dst_pred = np.argmax(model.model.predict(pert_data), axis=1)
dst_acc = accuracy_score(pert_true, dst_pred)
print '\tPerturbed Accuracy: %.4f' % dst_acc
if args.train_new:
# Train a new classifier with the new training data, test with original test data.
raise NotImplementedError(
'Training new classifier is not yet implemented.')
def load_model(self,
dataset="mnist",
model_name="2-layer",
model=None,
batch_size=0,
compute_slope=False):
"""
model: if set to None, then load dataset with model_name. Otherwise use the model directly.
dataset: mnist, cifar and imagenet. recommend to use mnist and cifar as a starting point.
model_name: possible options are 2-layer, distilled, and normal
"""
import tensorflow as tf
from setup_cifar import CIFAR, CIFARModel, TwoLayerCIFARModel
from setup_mnist import MNIST, MNISTModel, TwoLayerMNISTModel
from setup_imagenet import ImageNet, ImageNetModel
# if set this to true, we will use the logit layer output instead of probability
# the logit layer's gradients are usually larger and more stable
output_logits = True
self.dataset = dataset
self.model_name = model_name
if model is None:
print('Loading model...')
if dataset == "mnist":
self.batch_size = 1024
if model_name == "2-layer":
model = TwoLayerMNISTModel("models/mnist_2layer",
self.sess, not output_logits)
elif model_name == "normal":
model = MNISTModel("models/mnist", self.sess,
not output_logits)
elif model_name == "brelu":
model = MNISTModel("models/mnist_brelu",
self.sess,
not output_logits,
use_brelu=True)
elif model_name == "distilled":
model = MNISTModel("models/mnist-distilled-100", self.sess,
not output_logits)
else:
raise (RuntimeError("incorrect model option"))
elif dataset == "cifar":
self.batch_size = 1024
if model_name == "2-layer":
model = TwoLayerCIFARModel("models/cifar_2layer",
self.sess, not output_logits)
elif model_name == "normal":
model = CIFARModel("models/cifar", self.sess,
not output_logits)
elif model_name == "brelu":
model = CIFARModel("models/cifar_brelu",
self.sess,
not output_logits,
use_brelu=True)
elif model_name == "distilled":
model = CIFARModel("models/cifar-distilled-100", self.sess,
not output_logits)
else:
raise (RuntimeError("incorrect model option"))
elif dataset == "imagenet":
self.batch_size = 32
model = ImageNetModel(self.sess,
use_softmax=not output_logits,
model_name=model_name,
create_prediction=False)
else:
raise (RuntimeError("dataset unknown"))
self.model = model
self.compute_slope = compute_slope
if batch_size != 0:
self.batch_size = batch_size
# img is the placeholder for image input
self.img = tf.placeholder(shape=[
None, model.image_size, model.image_size, model.num_channels
],
dtype=tf.float32)
# output is the output tensor of the entire network
self.output = model.predict(self.img)
# create the graph to compute gradient
# get the desired true label and target label
self.true_label = tf.placeholder(dtype=tf.int32, shape=[])
self.target_label = tf.placeholder(dtype=tf.int32, shape=[])
true_output = self.output[:, self.true_label]
target_output = self.output[:, self.target_label]
# get the different
self.objective = true_output - target_output
# get the gradient
self.grad_op = tf.gradients(self.objective, self.img)[0]
# compute gradient norm
grad_op_rs = tf.reshape(self.grad_op, (tf.shape(self.grad_op)[0], -1))
self.grad_2_norm_op = tf.norm(grad_op_rs, axis=1)
self.grad_1_norm_op = tf.norm(grad_op_rs, ord=1, axis=1)
self.grad_inf_norm_op = tf.norm(grad_op_rs, ord=np.inf, axis=1)
return self.img, self.output
if __name__ == "__main__":
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
#the path of storing UAE
dir_adv = 'unsupervised_attack/'
#load model of Autoencoder
dir_model = 'models/MNIST/convAE'
data = MNIST()
inputs = data.train_data
tf.reset_default_graph()
with tf.Session(config=config) as sess:
model = MNISTModel(dir_model, sess)
attack = MINE_unsupervised(sess,
model,
batch_size=1,
max_iterations=40,
confidence=0,
epsilon=1.0,
mine_batch='conv')
adv, Mi = attack.attack(inputs)
np.save(dir_adv + 'adv.npy', image)
np.save(dir_adv + 'mi.npy', Mi)
tf.get_default_graph().finalize()
def main(args):
temp_encoder = encoder(level=args['level'])
with tf.Session() as sess:
use_log = not args['use_zvalue']
is_inception = args['dataset'] == "imagenet"
# load network
print('Loading model', args['dataset'])
if args['dataset'] == "mnist":
data, model = MNIST(), MNISTModel("models/mnist", sess, use_log)
# data, model = MNIST(), MNISTModel("models/mnist-distilled-100", sess, use_log)
elif args['dataset'] == "cifar10":
#data, model = CIFAR(), CIFARModel("models/cifar", sess, use_log)
# data, model = CIFAR(), CIFARModel("models/cifar-distilled-100", sess, use_log)
data, model = CIFAR(), CIFAR_WIDE("models/wide_resnet", sess,
use_log)
elif args['dataset'] == "imagenet":
data, model = ImageNet(), InceptionModel(sess, use_log)
print('Done...')
if args['numimg'] == 0:
args['numimg'] = len(data.test_labels) - args['firstimg']
print('Using', args['numimg'], 'test images')
# load attack module
if args['attack'] == "white":
# batch size 1, optimize on 1 image at a time, rather than optimizing images jointly
attack = CarliniL2(sess,
model,
batch_size=1,
max_iterations=args['maxiter'],
print_every=args['print_every'],
early_stop_iters=args['early_stop_iters'],
confidence=0,
learning_rate=args['lr'],
initial_const=args['init_const'],
binary_search_steps=args['binary_steps'],
targeted=not args['untargeted'],
use_log=use_log,
adam_beta1=args['adam_beta1'],
adam_beta2=args['adam_beta2'])
else:
# batch size 128, optimize on 128 coordinates of a single image
attack = BlackBoxL2(sess,
model,
batch_size=128,
max_iterations=args['maxiter'],
print_every=args['print_every'],
early_stop_iters=args['early_stop_iters'],
confidence=0,
learning_rate=args['lr'],
initial_const=args['init_const'],
binary_search_steps=args['binary_steps'],
targeted=not args['untargeted'],
use_log=use_log,
use_tanh=args['use_tanh'],
use_resize=args['use_resize'],
adam_beta1=args['adam_beta1'],
adam_beta2=args['adam_beta2'],
reset_adam_after_found=args['reset_adam'],
solver=args['solver'],
save_ckpts=args['save_ckpts'],
load_checkpoint=args['load_ckpt'],
start_iter=args['start_iter'],
init_size=args['init_size'],
use_importance=not args['uniform'])
random.seed(args['seed'])
np.random.seed(args['seed'])
print('Generate data')
all_inputs, all_targets, all_labels, all_true_ids, encoding_all = generate_data(
data,
samples=args['numimg'],
targeted=not args['untargeted'],
start=args['firstimg'],
inception=is_inception)
print('Done...')
#print('all_inputs : ', all_inputs.shape)
#print('encoding_all : ',encoding_all.shape)
os.system("mkdir -p {}/{}".format(args['save'], args['dataset']))
img_no = 0
total_success = 0
l2_total = 0.0
origin_correct = 0
adv_correct = 0
for i in range(all_true_ids.size):
print(' adversarial_image_no: ', i)
inputs = all_inputs[i:i + 1]
encoding_inputs = encoding_all[i:i + 1]
#print('encoding_inputs shape: ', encoding_inputs)
targets = all_targets[i:i + 1]
labels = all_labels[i:i + 1]
print("true labels:", np.argmax(labels), labels)
print("target:", np.argmax(targets), targets)
# test if the image is correctly classified
original_predict = model.model.predict(encoding_inputs)
original_predict = np.squeeze(original_predict)
original_prob = np.sort(original_predict)
original_class = np.argsort(original_predict)
print("original probabilities:", original_prob[-1:-6:-1])
print("original classification:", original_class[-1:-6:-1])
print("original probabilities (most unlikely):", original_prob[:6])
print("original classification (most unlikely):",
original_class[:6])
if original_class[-1] != np.argmax(labels):
print(
"skip wrongly classified image no. {}, original class {}, classified as {}"
.format(i, np.argmax(labels), original_class[-1]))
continue
origin_correct += np.argmax(labels, 1) == original_class[-1]
img_no += 1
timestart = time.time()
adv, const = attack.attack_batch(inputs, targets)
if type(const) is list:
const = const[0]
if len(adv.shape) == 3:
adv = adv.reshape((1, ) + adv.shape)
timeend = time.time()
l2_distortion = np.sum((adv - inputs)**2)**.5
##### llj
encode_adv = np.transpose(adv, axes=(0, 3, 1, 2))
channel0, channel1, channel2 = encode_adv[:,
0, :, :], encode_adv[:,
1, :, :], encode_adv[:,
2, :, :]
channel0, channel1, channel2 = temp_encoder.tempencoding(
channel0), temp_encoder.tempencoding(
channel1), temp_encoder.tempencoding(channel2)
encode_adv = np.concatenate([channel0, channel1, channel2], axis=1)
encode_adv = np.transpose(encode_adv, axes=(0, 2, 3, 1))
#### llj
adversarial_predict = model.model.predict(encode_adv)
adversarial_predict = np.squeeze(adversarial_predict)
adversarial_prob = np.sort(adversarial_predict)
adversarial_class = np.argsort(adversarial_predict)
print("adversarial probabilities:", adversarial_prob[-1:-6:-1])
print("adversarial classification:", adversarial_class[-1:-6:-1])
adv_correct += np.argmax(labels, 1) == adversarial_class[-1]
success = False
if args['untargeted']:
if adversarial_class[-1] != original_class[-1]:
success = True
else:
if adversarial_class[-1] == np.argmax(targets):
success = True
if l2_distortion > 20.0:
success = False
if success:
total_success += 1
l2_total += l2_distortion
suffix = "id{}_seq{}_prev{}_adv{}_{}_dist{}".format(
all_true_ids[i], i, original_class[-1], adversarial_class[-1],
success, l2_distortion)
print("Saving to", suffix)
show(
inputs,
"{}/{}/{}_original_{}.png".format(args['save'],
args['dataset'], img_no,
suffix))
show(
adv,
"{}/{}/{}_adversarial_{}.png".format(args['save'],
args['dataset'], img_no,
suffix))
show(
adv - inputs,
"{}/{}/{}_diff_{}.png".format(args['save'], args['dataset'],
img_no, suffix))
print(
"[STATS][L1] total = {}, seq = {}, id = {}, time = {:.3f}, success = {}, const = {:.6f}, prev_class = {}, new_class = {}, distortion = {:.5f}, success_rate = {:.3f}, l2_avg = {:.5f}"
.format(img_no, i, all_true_ids[i], timeend - timestart,
success, const, original_class[-1],
adversarial_class[-1], l2_distortion,
total_success / float(img_no),
0 if total_success == 0 else l2_total / total_success))
sys.stdout.flush()
print(' origin accuracy : ',
100.0 * origin_correct / all_true_ids.size)
print(' adv accuracy : ', 100.0 * adv_correct / all_true_ids.size)
def main(args):
with tf.Session() as sess:
random.seed(SEED)
np.random.seed(SEED)
tf.set_random_seed(SEED)
image_id_set = np.random.choice(range(1000),
args["image_number"] * 3,
replace=False)
#image_id_set = np.random.randint(1, 1000, args["image_number"] )
arg_max_iter = args['maxiter'] ### max number of iterations
arg_init_const = args[
'init_const'] ### regularization prior to attack loss
arg_kappa = args['kappa'] ### attack confidence level
arg_q = args['q'] ### number of random direction vectors
arg_mode = args['mode'] ### algorithm name
arg_save_iteration = args['save_iteration']
arg_Dataset = args["dataset"]
arg_targeted_attack = args["targeted_attack"]
arg_bsz = args["mini_batch_sz"]
idx_lr = args["lr_idx"]
## load classofier For MNIST and CIFAR pixel value range is [-0.5,0.5]
if (arg_Dataset == 'mnist'):
data, model = MNIST(), MNISTModel("models/mnist", sess, True)
elif (arg_Dataset == 'cifar10'):
data, model = CIFAR(), CIFARModel("models/cifar", sess, True)
elif (arg_Dataset == 'imagenet'):
data, model = ImageNet(SEED), InceptionModel(sess, True)
else:
print('Please specify a valid dataset')
succ_count, ii, iii = 0, 0, 0
final_distortion_count,first_iteration_count, first_distortion_count = [], [], []
while iii < args["image_number"]:
ii = ii + 1
image_id = image_id_set[ii]
# if image_id!= 836: continue # for test only
orig_prob, orig_class, orig_prob_str = util.model_prediction(
model, np.expand_dims(data.test_data[image_id],
axis=0)) ## orig_class: predicted label;
if arg_targeted_attack: ### target attack
target_label = np.remainder(orig_class + 1, 10)
else:
target_label = orig_class
orig_img, target = util.generate_data(data, image_id, target_label)
# shape of orig_img is (1,28,28,1) in [-0.5, 0.5]
true_label_list = np.argmax(data.test_labels, axis=1)
true_label = true_label_list[image_id]
print("Image ID:{}, infer label:{}, true label:{}".format(
image_id, orig_class, true_label))
if true_label != orig_class:
print(
"True Label is different from the original prediction, pass!"
)
continue
else:
iii = iii + 1
print('\n', iii, '/', args["image_number"])
## parameter
d = orig_img.size # feature dim
print("dimension = ", d)
# mu=1/d**2 # smoothing parameter
q = arg_q + 0
I = arg_max_iter + 0
kappa = arg_kappa + 0
const = arg_init_const + 0
## flatten image to vec
orig_img_vec = np.resize(orig_img, (1, d))
delta_adv = np.zeros((1, d)) ### initialized adv. perturbation
#delta_adv = np.random.uniform(-16/255,16/255,(1,d))
## w adv image initialization
if args["constraint"] == 'uncons':
# * 0.999999 to avoid +-0.5 return +-infinity
w_ori_img_vec = np.arctanh(
2 * (orig_img_vec) * 0.999999
) # in real value, note that orig_img_vec in [-0.5, 0.5]
w_img_vec = np.arctanh(
2 * (np.clip(orig_img_vec + delta_adv, -0.5, 0.5)) *
0.999999)
else:
w_ori_img_vec = orig_img_vec.copy()
w_img_vec = np.clip(w_ori_img_vec + delta_adv, -0.5, 0.5)
# ## test ##
# for test_value in w_ori_img_vec[0, :]:
# if np.isnan(test_value) or np.isinf(test_value):
# print(test_value)
# initialize the best solution & best loss
best_adv_img = [] # successful adv image in [-0.5, 0.5]
best_delta = [] # best perturbation
best_distortion = (0.5 * d)**2 # threshold for best perturbation
total_loss = np.zeros(I) ## I: max iters
l2s_loss_all = np.zeros(I)
attack_flag = False
first_flag = True ## record first successful attack
# parameter setting for ZO gradient estimation
mu = args["mu"] ### smoothing parameter
## learning rate
base_lr = args["lr"]
if arg_mode == "ZOAdaMM":
## parameter initialization for AdaMM
v_init = 1e-7 #0.00001
v_hat = v_init * np.ones((1, d))
v = v_init * np.ones((1, d))
m = np.zeros((1, d))
# momentum parameter for first and second order moment
beta_1 = 0.9
beta_2 = 0.9 # only used by AMSGrad
print(beta_1, beta_2)
#for i in tqdm(range(I)):
for i in range(I):
if args["decay_lr"]:
base_lr = args["lr"] / np.sqrt(i + 1)
## Total loss evaluation
if args["constraint"] == 'uncons':
total_loss[i], l2s_loss_all[
i] = function_evaluation_uncons(
w_img_vec, kappa, target_label, const, model,
orig_img, arg_targeted_attack)
else:
total_loss[i], l2s_loss_all[i] = function_evaluation_cons(
w_img_vec, kappa, target_label, const, model, orig_img,
arg_targeted_attack)
## gradient estimation w.r.t. w_img_vec
if arg_mode == "ZOSCD":
grad_est = grad_coord_estimation(mu, q, w_img_vec, d,
kappa, target_label,
const, model, orig_img,
arg_targeted_attack,
args["constraint"])
elif arg_mode == "ZONES":
grad_est = gradient_estimation_NES(mu, q, w_img_vec, d,
kappa, target_label,
const, model, orig_img,
arg_targeted_attack,
args["constraint"])
else:
grad_est = gradient_estimation_v2(mu, q, w_img_vec, d,
kappa, target_label,
const, model, orig_img,
arg_targeted_attack,
args["constraint"])
# if np.remainder(i,50)==0:
# print("total loss:",total_loss[i])
# print(np.linalg.norm(grad_est, np.inf))
## ZO-Attack, unconstrained optimization formulation
if arg_mode == "ZOSGD":
delta_adv = delta_adv - base_lr * grad_est
if arg_mode == "ZOsignSGD":
delta_adv = delta_adv - base_lr * np.sign(grad_est)
if arg_mode == "ZOSCD":
delta_adv = delta_adv - base_lr * grad_est
if arg_mode == "ZOAdaMM":
m = beta_1 * m + (1 - beta_1) * grad_est
v = beta_2 * v + (1 - beta_2) * np.square(grad_est) ### vt
v_hat = np.maximum(v_hat, v)
#print(np.mean(v_hat))
delta_adv = delta_adv - base_lr * m / np.sqrt(v_hat)
if args["constraint"] == 'cons':
tmp = delta_adv.copy()
#X_temp = orig_img_vec.reshape((-1,1))
#V_temp2 = np.diag(np.sqrt(v_hat.reshape(-1)+1e-10))
V_temp = np.sqrt(v_hat.reshape(1, -1))
delta_adv = projection_box(tmp, orig_img_vec, V_temp,
-0.5, 0.5)
#delta_adv2 = projection_box_2(tmp, X_temp, V_temp2, -0.5, 0.5)
# v_init = 1e-2 #0.00001
# v = v_init * np.ones((1, d))
# m = np.zeros((1, d))
# # momentum parameter for first and second order moment
# beta_1 = 0.9
# beta_2 = 0.99 # only used by AMSGrad
# m = beta_1 * m + (1-beta_1) * grad_est
# v = np.maximum(beta_2 * v + (1-beta_2) * np.square(grad_est),v)
# delta_adv = delta_adv - base_lr * m /np.sqrt(v+1e-10)
# if args["constraint"] == 'cons':
# V_temp = np.diag(np.sqrt(v.reshape(-1)+1e-10))
# X_temp = orig_img_vec.reshape((-1,1))
# delta_adv = projection_box(delta_adv, X_temp, V_temp, -0.5, 0.5)
if arg_mode == "ZOSMD":
delta_adv = delta_adv - 0.5 * base_lr * grad_est
# delta_adv = delta_adv - base_lr* grad_est
if args["constraint"] == 'cons':
#V_temp = np.eye(orig_img_vec.size)
V_temp = np.ones_like(orig_img_vec)
#X_temp = orig_img_vec.reshape((-1,1))
delta_adv = projection_box(delta_adv, orig_img_vec,
V_temp, -0.5, 0.5)
if arg_mode == "ZOPSGD":
delta_adv = delta_adv - base_lr * grad_est
if args["constraint"] == 'cons':
#V_temp = np.eye(orig_img_vec.size)
V_temp = np.ones_like(orig_img_vec)
#X_temp = orig_img_vec.reshape((-1,1))
delta_adv = projection_box(delta_adv, orig_img_vec,
V_temp, -0.5, 0.5)
if arg_mode == "ZONES":
delta_adv = delta_adv - base_lr * np.sign(grad_est)
if args["constraint"] == 'cons':
#V_temp = np.eye(orig_img_vec.size)
V_temp = np.ones_like(orig_img_vec)
#X = orig_img_vec.reshape((-1,1))
delta_adv = projection_box(delta_adv, orig_img_vec,
V_temp, -0.5, 0.5)
# if arg_mode == "ZO-AdaFom":
# m = beta_1 * m + (1-beta_1) * grad_est
# v = v* (float(i)/(i+1)) + np.square(grad_est)/(i+1)
# w_img_vec = w_img_vec - base_lr * m/np.sqrt(v)
##
### adv. example update
w_img_vec = w_ori_img_vec + delta_adv
## covert back to adv_img in [-0.5 , 0.5]
if args["constraint"] == 'uncons':
adv_img_vec = 0.5 * np.tanh((w_img_vec)) / 0.999999 #
else:
adv_img_vec = w_img_vec.copy()
adv_img = np.resize(adv_img_vec, orig_img.shape)
## update the best solution in the iterations
attack_prob, _, _ = util.model_prediction(model, adv_img)
target_prob = attack_prob[0, target_label]
attack_prob_tmp = attack_prob.copy()
attack_prob_tmp[0, target_label] = 0
other_prob = np.amax(attack_prob_tmp)
if args["print_iteration"]:
if np.remainder(i + 1, 1) == 0:
if true_label != np.argmax(attack_prob):
print(
"Iter %d (Succ): ID = %d, lr = %3.5f, decay = %d, ZO = %s %s, loss = %3.5f, l2sdist = %3.5f, TL = %d, PL = %d"
% (i + 1, image_id, args["lr"],
int(args["decay_lr"]), arg_mode,
args["constraint"], total_loss[i],
l2s_loss_all[i], true_label,
np.argmax(attack_prob)))
else:
print(
"Iter %d (Fail): ID = %d, lr = %3.6f, decay = %d, ZO = %s %s, loss = %3.5f, l2sdist = %3.5f, TL = %d, PL = %d"
% (i + 1, image_id, args["lr"],
int(args["decay_lr"]), arg_mode,
args["constraint"], total_loss[i],
l2s_loss_all[i], true_label,
np.argmax(attack_prob)))
if arg_save_iteration:
os.system("mkdir Examples")
if (np.logical_or(
true_label != np.argmax(attack_prob),
np.remainder(i + 1,
10) == 0)): ## every 10 iterations
suffix = "id_{}_Mode_{}_True_{}_Pred_{}_Ite_{}".format(
image_id, arg_mode, true_label,
np.argmax(attack_prob), i + 1)
# util.save_img(adv_img, "Examples/{}.png".format(suffix))
if arg_targeted_attack:
if (np.log(target_prob + 1e-10) -
np.log(other_prob + 1e-10) >=
kappa): # check attack confidence
if (distortion(adv_img, orig_img) <
best_distortion): # check distortion
# print('best distortion obtained at',i,'-th iteration')
best_adv_img = adv_img
best_distortion = distortion(adv_img, orig_img)
best_delta = adv_img - orig_img
best_iteration = i + 1
adv_class = np.argmax(attack_prob)
attack_flag = True
## Record first attack
if (first_flag):
first_flag = False ### once gets into this, it will no longer record the next sucessful attack
first_adv_img = adv_img
first_distortion = distortion(
adv_img, orig_img)
first_delta = adv_img - orig_img
first_class = adv_class
first_iteration = i + 1
else:
if (np.log(other_prob + 1e-10) -
np.log(target_prob + 1e-10) >=
kappa): # check attack confidence
if (distortion(adv_img, orig_img) <
best_distortion): # check distortion
# print('best distortion obtained at',i,'-th iteration')
best_adv_img = adv_img
best_distortion = distortion(adv_img, orig_img)
best_delta = adv_img - orig_img
best_iteration = i + 1
adv_class = np.argmax(attack_prob)
attack_flag = True
## Record first attack
if (first_flag):
first_flag = False
first_adv_img = adv_img
first_distortion = distortion(
adv_img, orig_img)
first_delta = adv_img - orig_img
first_class = adv_class
first_iteration = i + 1
if (attack_flag):
# os.system("mkdir Results_SL")
# ## best attack (final attack)
# suffix = "id_{}_Mode_{}_True_{}_Pred_{}".format(image_id, arg_mode, true_label, orig_class) ## orig_class, predicted label
# suffix2 = "id_{}_Mode_{}_True_{}_Pred_{}".format(image_id, arg_mode, true_label, adv_class)
# suffix3 = "id_{}_Mode_{}".format(image_id, arg_mode)
# ### save original image
# util.save_img(orig_img, "Results_SL/id_{}.png".format(image_id))
# util.save_img(orig_img, "Results_SL/{}_Orig.png".format(suffix))
# ### adv. image
# util.save_img(best_adv_img, "Results_SL/{}_Adv_best.png".format(suffix2))
# ### adv. perturbation
# util.save_img(best_delta, "Results_SL/{}_Delta_best.png".format(suffix3))
#
#
# ## first attack
# suffix4 = "id_{}_Mode_{}_True_{}_Pred_{}".format(image_id, arg_mode, true_label, first_class)
# ## first adv. imag
# util.save_img(first_adv_img, "Results_SL/{}_Adv_first.png".format(suffix4))
# ### first adv. perturbation
# util.save_img(first_delta, "Results_SL/{}_Delta_first.png".format(suffix3))
## save data
succ_count = succ_count + 1
final_distortion_count.append(l2s_loss_all[-1])
first_distortion_count.append(first_distortion)
first_iteration_count.append(first_iteration)
suffix0 = "retperimage2/id_{}_Mode_{}_{}_lr_{}_decay_{}_case{}_per".format(
image_id, arg_mode, args["constraint"], args["lr"],
int(args["decay_lr"]), args["exp_code"])
np.savez("{}".format(suffix0),
id=image_id,
mode=arg_mode,
loss=total_loss,
perturbation=l2s_loss_all,
best_distortion=best_distortion,
first_distortion=first_distortion,
first_iteration=first_iteration,
best_iteation=best_iteration,
learn_rate=args["lr"],
decay_lr=args["decay_lr"],
attack_flag=attack_flag)
## print
print("It takes {} iteations to find the first attack".format(
first_iteration))
# print(total_loss)
else:
## save data
suffix0 = "retperimage2/id_{}_Mode_{}_{}_lr_{}_decay_{}_case{}_per".format(
image_id, arg_mode, args["constraint"], args["lr"],
int(args["decay_lr"]), args["exp_code"])
np.savez("{}".format(suffix0),
id=image_id,
mode=arg_mode,
loss=total_loss,
perturbation=l2s_loss_all,
best_distortion=best_distortion,
learn_rate=args["lr"],
decay_lr=args["decay_lr"],
attack_flag=attack_flag)
print("Attack Fails")
sys.stdout.flush()
print('succ rate:', succ_count / args["image_number"])
print('average first success l2', np.mean(first_distortion_count))
print('average first itrs', np.mean(first_iteration_count))
print('average l2:', np.mean(final_distortion_count), ' best l2:',
np.min(final_distortion_count), ' worst l2:',
np.max(final_distortion_count))
def main(args):
with tf.Session() as sess:
if (args['dataset'] == 'mnist'):
data = MNIST()
inception = False
if (args['adversarial'] != "none"):
model = MNISTModel(
"models/mnist_cw" + str(args['adversarial']), sess)
elif (args['temp']):
model = MNISTModel(
"models/mnist-distilled-" + str(args['temp']), sess)
else:
model = MNISTModel("models/mnist", sess)
if (args['dataset'] == "cifar"):
data = CIFAR()
inception = False
if (args['adversarial'] != "none"):
model = CIFARModel(
"models/cifar_cw" + str(args['adversarial']), sess)
elif (args['temp']):
model = CIFARModel(
"models/cifar-distilled-" + str(args['temp']), sess)
else:
model = CIFARModel("models/cifar", sess)
if (args['dataset'] == "imagenet"):
data, model = ImageNet(args['seed_imagenet'],
2 * args['numimg']), InceptionModel(sess)
inception = True
inputs, targets, labels, true_ids = generate_data(
data,
model,
samples=args['numimg'],
targeted=not args['untargeted'],
target_num=args['targetnum'],
inception=inception,
train=args['train'],
seed=args['seed'])
timestart = time.time()
if (args['restore_np']):
if (args['train']):
adv = np.load(
str(args['dataset']) + '_' + str(args['attack']) +
'_train.npy')
else:
adv = np.load(
str(args['dataset']) + '_' + str(args['attack']) + '.npy')
else:
if (args['attack'] == 'L2'):
attack = CarliniL2(sess,
model,
batch_size=args['batch_size'],
max_iterations=args['maxiter'],
confidence=args['conf'],
initial_const=args['init_const'],
binary_search_steps=args['binary_steps'],
targeted=not args['untargeted'],
beta=args['beta'],
abort_early=args['abort_early'])
adv = attack.attack(inputs, targets)
if (args['attack'] == 'L1'):
attack = EADL1(sess,
model,
batch_size=args['batch_size'],
max_iterations=args['maxiter'],
confidence=args['conf'],
initial_const=args['init_const'],
binary_search_steps=args['binary_steps'],
targeted=not args['untargeted'],
beta=args['beta'],
abort_early=args['abort_early'])
adv = attack.attack(inputs, targets)
if (args['attack'] == 'EN'):
attack = EADEN(sess,
model,
batch_size=args['batch_size'],
max_iterations=args['maxiter'],
confidence=args['conf'],
initial_const=args['init_const'],
binary_search_steps=args['binary_steps'],
targeted=not args['untargeted'],
beta=args['beta'],
abort_early=args['abort_early'])
adv = attack.attack(inputs, targets)
"""If untargeted, pass labels instead of targets"""
if (args['attack'] == 'FGSM'):
attack = FGM(sess,
model,
batch_size=args['batch_size'],
ord=np.inf,
eps=args['eps'],
inception=inception)
adv = attack.attack(inputs, targets)
if (args['attack'] == 'FGML1'):
attack = FGM(sess,
model,
batch_size=args['batch_size'],
ord=1,
eps=args['eps'],
inception=inception)
adv = attack.attack(inputs, targets)
if (args['attack'] == 'FGML2'):
attack = FGM(sess,
model,
batch_size=args['batch_size'],
ord=2,
eps=args['eps'],
inception=inception)
adv = attack.attack(inputs, targets)
if (args['attack'] == 'IFGSM'):
attack = IFGM(sess,
model,
batch_size=args['batch_size'],
ord=np.inf,
eps=args['eps'],
inception=inception)
adv = attack.attack(inputs, targets)
if (args['attack'] == 'IFGML1'):
attack = IFGM(sess,
model,
batch_size=args['batch_size'],
ord=1,
eps=args['eps'],
inception=inception)
adv = attack.attack(inputs, targets)
if (args['attack'] == 'IFGML2'):
attack = IFGM(sess,
model,
batch_size=args['batch_size'],
ord=2,
eps=args['eps'],
inception=inception)
adv = attack.attack(inputs, targets)
timeend = time.time()
if args['untargeted']:
num_targets = 1
else:
num_targets = args['targetnum']
print("Took", timeend - timestart, "seconds to run",
len(inputs) / num_targets, "random instances.")
if (args['save_np']):
if (args['train']):
np.save(str(args['dataset']) + '_labels_train.npy', labels)
np.save(
str(args['dataset']) + '_' + str(args['attack']) +
'_train.npy', adv)
else:
np.save(
str(args['dataset']) + '_' + str(args['attack'] + '.npy'),
adv)
r_best_ = []
d_best_l1_ = []
d_best_l2_ = []
d_best_linf_ = []
r_average_ = []
d_average_l1_ = []
d_average_l2_ = []
d_average_linf_ = []
r_worst_ = []
d_worst_l1_ = []
d_worst_l2_ = []
d_worst_linf_ = []
#Transferability Tests
model_ = []
model_.append(model)
if (args['targetmodel'] != "same"):
if (args['targetmodel'] == "dd_100"):
model_.append(MNISTModel("models/mnist-distilled-100", sess))
num_models = len(model_)
if (args['show']):
if not os.path.exists(
str(args['save']) + "/" + str(args['dataset']) + "/" +
str(args['attack'])):
os.makedirs(
str(args['save']) + "/" + str(args['dataset']) + "/" +
str(args['attack']))
for m, model in enumerate(model_):
r_best = []
d_best_l1 = []
d_best_l2 = []
d_best_linf = []
r_average = []
d_average_l1 = []
d_average_l2 = []
d_average_linf = []
r_worst = []
d_worst_l1 = []
d_worst_l2 = []
d_worst_linf = []
for i in range(0, len(inputs), num_targets):
pred = []
for j in range(i, i + num_targets):
if inception:
pred.append(
np.reshape(model.model.predict(adv[j:j + 1]),
(data.test_labels[0:1].shape)))
else:
pred.append(model.model.predict(adv[j:j + 1]))
dist_l1 = 1e10
dist_l1_index = 1e10
dist_linf = 1e10
dist_linf_index = 1e10
dist_l2 = 1e10
dist_l2_index = 1e10
for k, j in enumerate(range(i, i + num_targets)):
success = False
if (args['untargeted']):
if (np.argmax(pred[k], 1) != np.argmax(
targets[j:j + 1], 1)):
success = True
else:
if (np.argmax(pred[k],
1) == np.argmax(targets[j:j + 1], 1)):
success = True
if (success):
if (np.sum(np.abs(adv[j] - inputs[j])) < dist_l1):
dist_l1 = np.sum(np.abs(adv[j] - inputs[j]))
dist_l1_index = j
if (np.amax(np.abs(adv[j] - inputs[j])) < dist_linf):
dist_linf = np.amax(np.abs(adv[j] - inputs[j]))
dist_linf_index = j
if ((np.sum((adv[j] - inputs[j])**2)**.5) < dist_l2):
dist_l2 = (np.sum((adv[j] - inputs[j])**2)**.5)
dist_l2_index = j
if (dist_l1_index != 1e10):
d_best_l2.append((np.sum(
(adv[dist_l2_index] - inputs[dist_l2_index])**2)**.5))
d_best_l1.append(
np.sum(
np.abs(adv[dist_l1_index] -
inputs[dist_l1_index])))
d_best_linf.append(
np.amax(
np.abs(adv[dist_linf_index] -
inputs[dist_linf_index])))
r_best.append(1)
else:
r_best.append(0)
rand_int = np.random.randint(i, i + num_targets)
if inception:
pred_r = np.reshape(
model.model.predict(adv[rand_int:rand_int + 1]),
(data.test_labels[0:1].shape))
else:
pred_r = model.model.predict(adv[rand_int:rand_int + 1])
success_average = False
if (args['untargeted']):
if (np.argmax(pred_r, 1) != np.argmax(
targets[rand_int:rand_int + 1], 1)):
success_average = True
else:
if (np.argmax(pred_r, 1) == np.argmax(
targets[rand_int:rand_int + 1], 1)):
success_average = True
if success_average:
r_average.append(1)
d_average_l2.append(
np.sum((adv[rand_int] - inputs[rand_int])**2)**.5)
d_average_l1.append(
np.sum(np.abs(adv[rand_int] - inputs[rand_int])))
d_average_linf.append(
np.amax(np.abs(adv[rand_int] - inputs[rand_int])))
else:
r_average.append(0)
dist_l1 = 0
dist_l1_index = 1e10
dist_linf = 0
dist_linf_index = 1e10
dist_l2 = 0
dist_l2_index = 1e10
for k, j in enumerate(range(i, i + num_targets)):
failure = True
if (args['untargeted']):
if (np.argmax(pred[k], 1) != np.argmax(
targets[j:j + 1], 1)):
failure = False
else:
if (np.argmax(pred[k],
1) == np.argmax(targets[j:j + 1], 1)):
failure = False
if failure:
r_worst.append(0)
dist_l1_index = 1e10
dist_l2_index = 1e10
dist_linf_index = 1e10
break
else:
if (np.sum(np.abs(adv[j] - inputs[j])) > dist_l1):
dist_l1 = np.sum(np.abs(adv[j] - inputs[j]))
dist_l1_index = j
if (np.amax(np.abs(adv[j] - inputs[j])) > dist_linf):
dist_linf = np.amax(np.abs(adv[j] - inputs[j]))
dist_linf_index = j
if ((np.sum((adv[j] - inputs[j])**2)**.5) > dist_l2):
dist_l2 = (np.sum((adv[j] - inputs[j])**2)**.5)
dist_l2_index = j
if (dist_l1_index != 1e10):
d_worst_l2.append((np.sum(
(adv[dist_l2_index] - inputs[dist_l2_index])**2)**.5))
d_worst_l1.append(
np.sum(
np.abs(adv[dist_l1_index] -
inputs[dist_l1_index])))
d_worst_linf.append(
np.amax(
np.abs(adv[dist_linf_index] -
inputs[dist_linf_index])))
r_worst.append(1)
if (args['show'] and m == (num_models - 1)):
for j in range(i, i + num_targets):
target_id = np.argmax(targets[j:j + 1], 1)
label_id = np.argmax(labels[j:j + 1], 1)
prev_id = np.argmax(
np.reshape(model.model.predict(inputs[j:j + 1]),
(data.test_labels[0:1].shape)), 1)
adv_id = np.argmax(
np.reshape(model.model.predict(adv[j:j + 1]),
(data.test_labels[0:1].shape)), 1)
suffix = "id{}_seq{}_lbl{}_prev{}_adv{}_{}_l1_{:.3f}_l2_{:.3f}_linf_{:.3f}".format(
true_ids[i], target_id, label_id, prev_id, adv_id,
adv_id == target_id,
np.sum(np.abs(adv[j] - inputs[j])),
np.sum((adv[j] - inputs[j])**2)**.5,
np.amax(np.abs(adv[j] - inputs[j])))
show(
inputs[j:j + 1],
str(args['save']) + "/" + str(args['dataset']) +
"/" + str(args['attack']) +
"/original_{}.png".format(suffix))
show(
adv[j:j + 1],
str(args['save']) + "/" + str(args['dataset']) +
"/" + str(args['attack']) +
"/adversarial_{}.png".format(suffix))
if (m != (num_models - 1)):
lbl = "Src_"
if (num_models > 2):
lbl += str(m) + "_"
else:
lbl = "Tgt_"
if (num_targets > 1):
print(lbl + 'best_case_L1_mean', np.mean(d_best_l1))
print(lbl + 'best_case_L2_mean', np.mean(d_best_l2))
print(lbl + 'best_case_Linf_mean', np.mean(d_best_linf))
print(lbl + 'best_case_prob', np.mean(r_best))
print(lbl + 'average_case_L1_mean', np.mean(d_average_l1))
print(lbl + 'average_case_L2_mean', np.mean(d_average_l2))
print(lbl + 'average_case_Linf_mean', np.mean(d_average_linf))
print(lbl + 'average_case_prob', np.mean(r_average))
print(lbl + 'worst_case_L1_mean', np.mean(d_worst_l1))
print(lbl + 'worst_case_L2_mean', np.mean(d_worst_l2))
print(lbl + 'worst_case_Linf_mean', np.mean(d_worst_linf))
print(lbl + 'worst_case_prob', np.mean(r_worst))
else:
print(lbl + 'L1_mean', np.mean(d_average_l1))
print(lbl + 'L2_mean', np.mean(d_average_l2))
print(lbl + 'Linf_mean', np.mean(d_average_linf))
print(lbl + 'success_prob', np.mean(r_average))
def main(args):
with tf.Session() as sess:
if args['dataset'] == 'mnist':
data, model = MNIST(), MNISTModel("models/mnist", sess)
handpick = False
inception = False
if args['dataset'] == "cifar":
data, model = CIFAR(), CIFARModel("models/cifar", sess)
handpick = True
inception = False
if args['dataset'] == "imagenet":
data, model = ImageNet(args['seed_imagenet']), InceptionModel(sess)
handpick = True
inception = True
if args['adversarial'] != "none":
model = MNISTModel(
"models/mnist_cwl2_admm" + str(args['adversarial']), sess)
if args['temp'] and args['dataset'] == 'mnist':
model = MNISTModel("models/mnist-distilled-" + str(args['temp']),
sess)
if args['temp'] and args['dataset'] == 'cifar':
model = CIFARModel("models/cifar-distilled-" + str(args['temp']),
sess)
inputs, targets, labels, true_ids = generate_data(
data,
model,
samples=args['numimg'],
targeted=True,
start=0,
inception=inception,
handpick=handpick,
seed=args['seed'])
#print(true_ids)
if args['attack'] == 'L2C':
attack = CarliniL2(sess,
model,
batch_size=args['batch_size'],
max_iterations=args['maxiter'],
confidence=args['conf'],
binary_search_steps=args['binary_steps'],
abort_early=args['abort_early'])
if args['attack'] == 'L0A':
attack = ADMML0(sess,
model,
batch_size=args['batch_size'],
max_iterations=args['maxiter'],
confidence=args['conf'],
binary_search_steps=args['iteration_steps'],
ro=args['ro'],
abort_early=args['abort_early'])
timestart = time.time()
adv = attack.attack(inputs, targets)
timeend = time.time()
print("Took", timeend - timestart, "seconds to run", len(inputs),
"samples.\n")
if args['train']:
np.save('labels_train.npy', labels)
np.save(str(args['attack']) + '_train.npy', adv)
if (args['conf'] != 0):
model = MNISTModel("models/mnist-distilled-100", sess)
if args['attack'] != 'L0A' and args['attack'] != 'L0AE' and args[
'attack'] != 'L0C' and args['attack'] != 'L0AE2':
l1_l2_li_computation(args, data, model, adv, inception, inputs,
targets, labels, true_ids)
else:
l0_computation(args, data, model, adv, inception, inputs, targets,
labels, true_ids)
def run(args, restrict=True):
if restrict:
# Restrict the visible GPUs to the one for this subprocess
id = np.int(multiprocessing.current_process().name.split("-")[1])
os.environ["CUDA_VISIBLE_DEVICES"] = str(id - 1)
# Load Parameters
dataset = args[0]
epsilon = float(args[1])
mode = args[2]
K = int(args[3])
fname = dataset + "/" + str(epsilon) + "_" + mode + "_" + str(K)
# Configure Keras/Tensorflow
Keras.clear_session()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
set_session(tf.Session(config=config))
sess = Keras.get_session()
Keras.set_learning_phase(False)
# Fix Random Seeds
np.random.seed(1)
tf.set_random_seed(
1
) #Having this before keras.clear_session() causes it it hang for some reason
# Load Model/Data and setup SPSA placeholders
N = 50
if dataset == "MNIST":
# Base Model
base_model = MNISTModel("../1-Models/MNIST")
data = MNIST()
x = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
# SPSA
shape_spsa = (1, 28, 28, 1)
x_spsa = tf.placeholder(tf.float32, shape=shape_spsa)
elif dataset == "CIFAR":
# Base Model
base_model = CIFARModel("../1-Models/CIFAR")
data = CIFAR()
x = tf.placeholder(tf.float32, shape=(None, 32, 32, 3))
# SPSA
shape_spsa = (1, 32, 32, 3)
x_spsa = tf.placeholder(tf.float32, shape=shape_spsa)
y_spsa = tf.placeholder(tf.int32)
# Load the hidden representations of the real and adversarial examples from the training set
x_train_real = np.squeeze(
np.load("../3-Representation/" + dataset + "/train_" + mode + ".npy"))
x_train_adv = np.squeeze(
np.load("../3-Representation/" + dataset + "/train_adv_" + mode +
".npy"))
n_train = x_train_real.shape[0]
n_train_adv = x_train_adv.shape[0]
x_train = np.float32(np.vstack((x_train_real, x_train_adv)))
#print("Bounds ", np.max(np.abs(x_train)))
y_train = np.float32(
np.hstack((-1.0 * np.ones(n_train), np.ones(n_train_adv))))
# Create the defended model
model_defended = DefendedModel(base_model, x_train, y_train, K)
defended_logits = model_defended.get_logits(x)
# Configure the attack
attack = SPSA(model_defended, back="tf", sess=sess)
with tf.name_scope("Attack") as scope:
gen = attack.generate(x_spsa,
y=y_spsa,
epsilon=epsilon,
is_targeted=False,
num_steps=100,
batch_size=2048,
early_stop_loss_threshold=-5.0)
# Run the attack
f = open(fname + ".txt", "w")
sample = np.random.choice(data.test_data.shape[0], N, replace=False)
x_sample = data.test_data[sample]
y_sample = np.argmax(data.test_labels[sample], axis=1)
logits_nat = sess.run(defended_logits, {x: x_sample})
f.write("Accuracy on Natural Images: " +
str(np.mean(np.argmax(logits_nat, axis=1) == y_sample)) + "\n")
pred_adv = -1.0 * np.ones((N))
for i in range(N):
x_real = x_sample[i].reshape(shape_spsa)
x_adv = sess.run(gen, {x_spsa: x_real, y_spsa: y_sample[i]})
pred_adv[i] = np.argmax(sess.run(defended_logits, {x: x_adv}))
f.write("Accuracy on Adversarial Images: " +
str(np.mean(pred_adv == y_sample)))
f.close()
def train_sub(sess, x, y, bbox_preds, X_sub, Y_sub, nb_classes,
nb_epochs_s, batch_size, learning_rate, data_aug, lmbda):
"""
This function creates the substitute by alternatively
augmenting the training data and training the substitute.
:param sess: TF session
:param x: input TF placeholder
:param y: output TF placeholder
:param bbox_preds: output of black-box model predictions
:param X_sub: initial substitute training data
:param Y_sub: initial substitute training labels
:param nb_classes: number of output classes
:param nb_epochs_s: number of epochs to train substitute model
:param batch_size: size of training batches
:param learning_rate: learning rate for training
:param data_aug: number of times substitute training data is augmented
:param lmbda: lambda from arxiv.org/abs/1602.02697
:return:
"""
# Define TF model graph (for the black-box model)
# model_sub = substitute_model()
if DATASET == "mnist":
model_sub = MNISTModel(use_log = True).model
else:
model_sub = CIFARModel(use_log = True).model
preds_sub = model_sub(x)
print("Defined TensorFlow model graph for the substitute.")
# Define the Jacobian symbolically using TensorFlow
grads = jacobian_graph(preds_sub, x, nb_classes)
# Train the substitute and augment dataset alternatively
for rho in xrange(data_aug):
print("Substitute training epoch #" + str(rho))
train_params = {
'nb_epochs': nb_epochs_s,
'batch_size': batch_size,
'learning_rate': learning_rate
}
model_train(sess, x, y, preds_sub, X_sub, to_categorical(Y_sub),
init_all=False, verbose=False, args=train_params)
# If we are not at last substitute training iteration, augment dataset
if rho < data_aug - 1:
if FLAGS.cached_aug:
augs = np.load('sub_saved/{}-aug-{}.npz'.format(DATASET, rho))
X_sub = augs['X_sub']
Y_sub = augs['Y_sub']
else:
print("Augmenting substitute training data.")
# Perform the Jacobian augmentation
X_sub = jacobian_augmentation(sess, x, X_sub, Y_sub, grads, lmbda)
print("Labeling substitute training data.")
# Label the newly generated synthetic points using the black-box
Y_sub = np.hstack([Y_sub, Y_sub])
X_sub_prev = X_sub[int(len(X_sub)/2):]
eval_params = {'batch_size': batch_size}
bbox_val = batch_eval(sess, [x], [bbox_preds], [X_sub_prev],
args=eval_params)[0]
# Note here that we take the argmax because the adversary
# only has access to the label (not the probabilities) output
# by the black-box model
Y_sub[int(len(X_sub)/2):] = np.argmax(bbox_val, axis=1)
# cache the augmentation
if not FLAGS.cached_aug:
np.savez('sub_saved/{}-aug-{}.npz'.format(DATASET, rho), X_sub = X_sub, Y_sub = Y_sub)
return model_sub, preds_sub
def main(args):
with tf.Session() as sess:
random.seed(121)
np.random.seed(1211)
image_id = args['img_id']
arg_max_iter = args['maxiter']
arg_b = args['binary_steps']
arg_init_const = args['init_const']
arg_mode = args['mode']
arg_kappa = args['kappa']
arg_beta = args['beta']
arg_gamma = args['gamma']
AE_model = util.load_AE("mnist_AE_1")
data, model = MNIST(), MNISTModel("models/mnist", sess, False)
orig_prob, orig_class, orig_prob_str = util.model_prediction(
model, np.expand_dims(data.test_data[image_id], axis=0))
target_label = orig_class
print("Image:{}, infer label:{}".format(image_id, target_label))
orig_img, target = util.generate_data(data, image_id, target_label)
attack = AEADEN(sess,
model,
mode=arg_mode,
AE=AE_model,
batch_size=1,
kappa=arg_kappa,
init_learning_rate=1e-2,
binary_search_steps=arg_b,
max_iterations=arg_max_iter,
initial_const=arg_init_const,
beta=arg_beta,
gamma=arg_gamma)
adv_img = attack.attack(orig_img, target)
adv_prob, adv_class, adv_prob_str = util.model_prediction(
model, adv_img)
delta_prob, delta_class, delta_prob_str = util.model_prediction(
model, orig_img - adv_img)
INFO = "[INFO]id:{}, kappa:{}, Orig class:{}, Adv class:{}, Delta class: {}, Orig prob:{}, Adv prob:{}, Delta prob:{}".format(
image_id, arg_kappa, orig_class, adv_class, delta_class,
orig_prob_str, adv_prob_str, delta_prob_str)
print(INFO)
suffix = "id{}_kappa{}_Orig{}_Adv{}_Delta{}".format(
image_id, arg_kappa, orig_class, adv_class, delta_class)
arg_save_dir = "{}_ID{}_Gamma_{}".format(arg_mode, image_id, arg_gamma)
os.system("mkdir -p Results/{}".format(arg_save_dir))
util.save_img(
orig_img,
"Results/{}/Orig_original{}.png".format(arg_save_dir, orig_class))
util.save_img(adv_img,
"Results/{}/Adv_{}.png".format(arg_save_dir, suffix))
util.save_img(
np.absolute(orig_img - adv_img) - 0.5,
"Results/{}/Delta_{}.png".format(arg_save_dir, suffix))
sys.stdout.flush()
def run(args, restrict=True):
if restrict:
# Restrict the visible GPUs to the one for this subprocess
id = np.int(multiprocessing.current_process().name.split("-")[1])
os.environ["CUDA_VISIBLE_DEVICES"] = str(id - 1)
# Load Parameters
dataset = args[0]
epsilon = float(args[1])
mode = args[2]
K = int(args[3])
bias = float(args[4])
fname = dataset + "/" + str(epsilon) + "_" + mode + "_" + str(
K) + "_" + str(bias)
# Configure Keras/Tensorflow
Keras.clear_session()
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
set_session(tf.Session(config=config))
sess = Keras.get_session()
Keras.set_learning_phase(False)
# Fix Random Seeds
np.random.seed(1)
tf.set_random_seed(
1
) #Having this before keras.clear_session() causes it it hang for some reason
# Load Model/Data and setup SPSA placeholders
N = 1000
if dataset == "MNIST":
# Base Model
base_model = MNISTModel("../1-Models/MNIST")
data = MNIST()
x = tf.placeholder(tf.float32, shape=(None, 28, 28, 1))
# SPSA
shape_spsa = (1, 28, 28, 1)
x_spsa = tf.placeholder(tf.float32, shape=shape_spsa)
elif dataset == "CIFAR":
# Base Model
base_model = CIFARModel("../1-Models/CIFAR")
data = CIFAR()
x = tf.placeholder(tf.float32, shape=(None, 32, 32, 3))
# SPSA
shape_spsa = (1, 32, 32, 3)
x_spsa = tf.placeholder(tf.float32, shape=shape_spsa)
y_spsa = tf.placeholder(tf.int32)
# Load the hidden representations of the real and adversarial examples from the training set
x_train_real = np.squeeze(
np.load("../3-Representation/" + dataset + "/train_" + mode + ".npy"))
x_train_adv = np.squeeze(
np.load("../3-Representation/" + dataset + "/train_adv_" + mode +
".npy"))
n_train = x_train_real.shape[0]
n_train_adv = x_train_adv.shape[0]
x_train = np.float32(np.vstack((x_train_real, x_train_adv)))
#print("Bounds ", np.max(np.abs(x_train)))
y_train = np.float32(
np.hstack((-1.0 * np.ones(n_train), np.ones(n_train_adv))))
# Create the defended model
model_defended = DefendedModel(base_model, x_train, y_train, K, bias=bias)
defended_logits = model_defended.get_logits(x)
# Get the predictions on the original images
labels = np.argmax(data.test_labels[:N], axis=1)
logits_real = sess.run(defended_logits, {x: data.test_data[:N]})
fp = (np.argmax(logits_real,
axis=1) == 10) #False positives of the defense
pred_undefended = np.argmax(np.delete(logits_real, -1, axis=1),
axis=1) #Original model prediction
# Configure the attack
attack = SPSA(model_defended, back="tf", sess=sess)
with tf.name_scope("Attack") as scope:
gen = attack.generate(x_spsa,
y_target=y_spsa,
epsilon=epsilon,
is_targeted=True,
num_steps=100,
batch_size=2048,
early_stop_loss_threshold=-5.0)
# Run the attack
pred_adv = -1.0 * np.ones((N, 10))
for i in range(N):
if i % 10 == 0:
print(fname, " ", i)
out = {}
out["FP"] = fp
out["Labels"] = labels
out["UndefendedPrediction"] = pred_undefended
out["AdversarialPredictions"] = pred_adv
file = open(fname, "wb")
pickle.dump(out, file)
file.close()
x_real = data.test_data[i].reshape(shape_spsa)
# Try a targeted attack for each class other than the original network prediction and the adversarial class
for y in range(10):
if y != pred_undefended[i]:
x_adv = sess.run(gen, {x_spsa: x_real, y_spsa: y})
pred_adv[i,
y] = np.argmax(sess.run(defended_logits, {x: x_adv}))
out = {}
out["FP"] = fp
out["Labels"] = labels
out["UndefendedPrediction"] = pred_undefended
out["AdversarialPredictions"] = pred_adv
file = open(fname, "wb")
pickle.dump(out, file)
file.close()
analysis(fname)
## Copyright (C) 2016, Nicholas Carlini <*****@*****.**>.
##
## This program is licenced under the BSD 2-Clause licence,
## contained in the LICENCE file in this directory.
from setup_cifar import CIFAR, CIFARModel
from setup_mnist import MNIST, MNISTModel
from setup_inception import ImageNet, InceptionModel
import tensorflow as tf
import numpy as np
BATCH_SIZE = 1
with tf.Session() as sess:
data, model = MNIST(), MNISTModel("models/mnist", sess)
data, model = CIFAR(), CIFARModel("models/cifar", sess)
data, model = ImageNet(), InceptionModel(sess)
x = tf.placeholder(
tf.float32,
(None, model.image_size, model.image_size, model.num_channels))
y = model.predict(x)
r = []
for i in range(0, len(data.test_data), BATCH_SIZE):
pred = sess.run(y, {x: data.test_data[i:i + BATCH_SIZE]})
#print(pred)
#print('real',data.test_labels[i],'pred',np.argmax(pred))
r.append(
np.argmax(pred, 1) == np.argmax(data.test_labels[i:i +
def expandImage(image_data):
image_data2 = np.array(image_data)
image_data2 = (image_data2 + 0.5) * 255
return image_data2
# In[4]:
if __name__ == "__main__":
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
modelPath = '%smodels/mnist' % (nn_robust_attack_root)
data, model = MNIST(), MNISTModel(modelPath, sess)
attack = CarliniLi(sess, model, max_iterations=1000, targeted=False)
inputs, targets = generate_data(data,
samples=1000,
targeted=False,
start=5500,
inception=False)
original_classified_wrong_number = 0 #number of benign samples that are misclassified
disturbed_failure_number = 0 #number of samples that failed to craft corresponding adversarial samples
test_number = 0 #number of adversarial samples that we generate
TTP = 0
TP = 0
FN = 0
def main(args):
with tf.Session() as sess:
random.seed(SEED)
np.random.seed(SEED)
tf.set_random_seed(SEED)
class_id = args['class_id'] ### input image (natural example)
target_id = args[
'target_id'] ### target images id (adv example) if target attack
arg_max_iter = args['maxiter'] ### max number of iterations
arg_init_const = args[
'init_const'] ### regularization prior to attack loss
arg_kappa = args['kappa'] ### attack confidence level
arg_q = args['q'] ### number of random direction vectors
arg_mode = args['mode'] ### algorithm name
arg_save_iteration = args['save_iteration']
arg_Dataset = args["dataset"]
arg_targeted_attack = args["targeted_attack"]
arg_bsz = args["mini_batch_sz"]
idx_lr = args["lr_idx"]
## load classofier For MNIST and CIFAR pixel value range is [-0.5,0.5]
if (arg_Dataset == 'mnist'):
data, model = MNIST(), MNISTModel("models/mnist", sess, True)
elif (arg_Dataset == 'cifar10'):
data, model = CIFAR(), CIFARModel("models/cifar", sess, True)
elif (arg_Dataset == 'imagenet'):
data, model = ImageNet_Universal(SEED), InceptionModel(sess, True)
#model = InceptionModel(sess, True)
else:
print('Please specify a valid dataset')
#orig_img = np.load('ori_img_backup.npy')
orig_img = data.test_data[np.where(
np.argmax(data.test_labels, 1) == class_id)]
#np.save('ori_img_backup',orig_img)
#true_label = data.test_labels[np.where(np.argmax(data.test_labels,1) == class_id)]
_, orig_class = util.model_prediction_u(
model, orig_img[:30]
) # take 30 or less images to make sure arg_bsz number of them are valid
# filter out the images which misclassified already
orig_img = orig_img[np.where(orig_class == class_id)]
if orig_img.shape[0] < arg_bsz:
assert 'no enough valid inputs'
orig_img = orig_img[:arg_bsz]
np.save('original_imgsID' + str(class_id), orig_img)
#true_label = np.zeros((arg_bsz, 1001))
#true_label[np.arange(arg_bsz), class_id] = 1
true_label = class_id
if arg_targeted_attack: ### target attack
#target_label = np.zeros((arg_bsz, 1001))
#target_label[np.arange(arg_bsz), target_id] = 1
target_label = target_id
else:
target_label = true_label
#orig_img, target = util.generate_data(data, class_id, target_label)
# shape of orig_img is (1,28,28,1) in [-0.5, 0.5]
## parameter
if orig_img.ndim == 3 or orig_img.shape[0] == 1:
d = orig_img.size # feature dim
else:
d = orig_img[0].size
print("dimension = ", d)
# mu=1/d**2 # smoothing parameter
q = arg_q + 0
I = arg_max_iter + 0
kappa = arg_kappa + 0
const = arg_init_const + 0
## flatten image to vec
orig_img_vec = np.resize(orig_img, (arg_bsz, d))
## w adv image initialization
if args["constraint"] == 'uncons':
# * 0.999999 to avoid +-0.5 return +-infinity
w_ori_img_vec = np.arctanh(
2 * (orig_img_vec) * 0.999999
) # in real value, note that orig_img_vec in [-0.5, 0.5]
w_img_vec = w_ori_img_vec.copy()
else:
w_ori_img_vec = orig_img_vec.copy()
w_img_vec = w_ori_img_vec.copy()
# ## test ##
# for test_value in w_ori_img_vec[0, :]:
# if np.isnan(test_value) or np.isinf(test_value):
# print(test_value)
delta_adv = np.zeros((1, d)) ### initialized adv. perturbation
# initialize the best solution & best loss
best_adv_img = [] # successful adv image in [-0.5, 0.5]
best_delta = [] # best perturbation
best_distortion = (0.5 * d)**2 # threshold for best perturbation
total_loss = np.zeros(I) ## I: max iters
l2s_loss_all = np.zeros(I)
attack_flag = False
first_flag = True ## record first successful attack
# parameter setting for ZO gradient estimation
mu = args["mu"] ### smoothing parameter
## learning rate
base_lr = args["lr"]
if arg_mode == "ZOAdaMM":
## parameter initialization for AdaMM
v_init = 1e-7 #0.00001
v_hat = v_init * np.ones((1, d))
v = v_init * np.ones((1, d))
m = np.zeros((1, d))
# momentum parameter for first and second order moment
beta_1 = 0.9
beta_2 = 0.3 # only used by AMSGrad
print(beta_1, beta_2)
#for i in tqdm(range(I)):
for i in range(I):
if args["decay_lr"]:
base_lr = args["lr"] / np.sqrt(i + 1)
## Total loss evaluation
if args["constraint"] == 'uncons':
total_loss[i], l2s_loss_all[i] = function_evaluation_uncons(
w_img_vec, kappa, target_label, const, model, orig_img,
arg_targeted_attack)
else:
total_loss[i], l2s_loss_all[i] = function_evaluation_cons(
w_img_vec, kappa, target_label, const, model, orig_img,
arg_targeted_attack)
## gradient estimation w.r.t. w_img_vec
if arg_mode == "ZOSCD":
grad_est = grad_coord_estimation(mu, q, w_img_vec, d, kappa,
target_label, const, model,
orig_img, arg_targeted_attack,
args["constraint"])
elif arg_mode == "ZONES":
grad_est = gradient_estimation_NES(mu, q, w_img_vec, d, kappa,
target_label, const, model,
orig_img,
arg_targeted_attack,
args["constraint"])
else:
grad_est = gradient_estimation_v2(mu, q, w_img_vec, d, kappa,
target_label, const, model,
orig_img,
arg_targeted_attack,
args["constraint"])
# if np.remainder(i,50)==0:
# print("total loss:",total_loss[i])
# print(np.linalg.norm(grad_est, np.inf))
## ZO-Attack, unconstrained optimization formulation
if arg_mode == "ZOSGD":
delta_adv = delta_adv - base_lr * grad_est
if arg_mode == "ZOsignSGD":
delta_adv = delta_adv - base_lr * np.sign(grad_est)
if arg_mode == "ZOSCD":
delta_adv = delta_adv - base_lr * grad_est
if arg_mode == "ZOAdaMM":
m = beta_1 * m + (1 - beta_1) * grad_est
v = beta_2 * v + (1 - beta_2) * np.square(grad_est) ### vt
#print(np.mean(np.abs(m)),np.mean(np.sqrt(v)))
v_hat = np.maximum(v_hat, v)
delta_adv = delta_adv - base_lr * m / np.sqrt(v)
if args["constraint"] == 'cons':
tmp = delta_adv.copy()
#X_temp = orig_img_vec.reshape((-1,1))
#V_temp2 = np.diag(np.sqrt(v_hat.reshape(-1)+1e-10))
V_temp = np.sqrt(v_hat.reshape(1, -1))
delta_adv = projection_box(tmp, orig_img_vec, V_temp, -0.5,
0.5)
#delta_adv2 = projection_box_2(tmp, X_temp, V_temp2, -0.5, 0.5)
# v_init = 1e-2 #0.00001
# v = v_init * np.ones((1, d))
# m = np.zeros((1, d))
# # momentum parameter for first and second order moment
# beta_1 = 0.9
# beta_2 = 0.99 # only used by AMSGrad
# m = beta_1 * m + (1-beta_1) * grad_est
# v = np.maximum(beta_2 * v + (1-beta_2) * np.square(grad_est),v)
# delta_adv = delta_adv - base_lr * m /np.sqrt(v+1e-10)
# if args["constraint"] == 'cons':
# V_temp = np.diag(np.sqrt(v.reshape(-1)+1e-10))
# X_temp = orig_img_vec.reshape((-1,1))
# delta_adv = projection_box(delta_adv, X_temp, V_temp, -0.5, 0.5)
if arg_mode == "ZOSMD":
delta_adv = delta_adv - 0.5 * base_lr * grad_est
# delta_adv = delta_adv - base_lr* grad_est
if args["constraint"] == 'cons':
#V_temp = np.eye(orig_img_vec.size)
V_temp = np.ones((1, d))
#X_temp = orig_img_vec.reshape((-1,1))
delta_adv = projection_box(delta_adv, orig_img_vec, V_temp,
-0.5, 0.5)
if arg_mode == "ZOPSGD":
delta_adv = delta_adv - base_lr * grad_est
if args["constraint"] == 'cons':
#V_temp = np.eye(orig_img_vec.size)
V_temp = np.ones((1, d))
#X_temp = orig_img_vec.reshape((-1,1))
delta_adv = projection_box(delta_adv, orig_img_vec, V_temp,
-0.5, 0.5)
if arg_mode == "ZONES":
delta_adv = delta_adv - base_lr * np.sign(grad_est)
if args["constraint"] == 'cons':
#V_temp = np.eye(orig_img_vec.size)
V_temp = np.ones((1, d))
#X = orig_img_vec.reshape((-1,1))
delta_adv = projection_box(delta_adv, orig_img_vec, V_temp,
-0.5, 0.5)
# if arg_mode == "ZO-AdaFom":
# m = beta_1 * m + (1-beta_1) * grad_est
# v = v* (float(i)/(i+1)) + np.square(grad_est)/(i+1)
# w_img_vec = w_img_vec - base_lr * m/np.sqrt(v)
##
### adv. example update
w_img_vec = w_ori_img_vec + delta_adv
## covert back to adv_img in [-0.5 , 0.5]
if args["constraint"] == 'uncons':
adv_img_vec = 0.5 * np.tanh((w_img_vec)) / 0.999999 #
else:
adv_img_vec = w_img_vec.copy()
adv_img = np.resize(adv_img_vec, orig_img.shape)
## update the best solution in the iterations
attack_prob, _, _ = util.model_prediction(model, adv_img)
target_prob = attack_prob[:, target_label]
attack_prob_tmp = attack_prob.copy()
attack_prob_tmp[:, target_label] = 0
other_prob = np.amax(attack_prob_tmp, 1)
if i % 1000 == 0 and i != 0:
if arg_mode == "ZOAdaMM": print(beta_1, beta_2)
print("save delta_adv")
np.save(
'retimgs/' + str(i) + 'itrs' +
str(np.argmax(attack_prob, 1)) + arg_mode +
str(args["lr"]), delta_adv)
if args["print_iteration"]:
if np.remainder(i + 1, 20) == 0:
if (true_label != np.argmax(attack_prob, 1)).all():
print(
"Iter %d (Succ): ID = %d, lr = %3.7f, decay = %d, ZO = %s %s, loss = %3.5f, l2sdist = %3.5f, TL = %d, PL = %s"
% (i + 1, class_id, args["lr"],
int(args["decay_lr"]), arg_mode,
args["constraint"], total_loss[i],
l2s_loss_all[i], true_label,
np.argmax(attack_prob, 1)))
else:
sr = np.sum(
true_label != np.argmax(attack_prob, 1)) / arg_bsz
print(
"Iter %d (Fail): ID = %d, lr = %3.7f, decay = %d, ZO = %s %s, loss = %3.5f, l2sdist = %3.5f, TL = %d, PL = %s, succ rate = %.2f"
% (i + 1, class_id, args["lr"],
int(args["decay_lr"]), arg_mode,
args["constraint"], total_loss[i],
l2s_loss_all[i], true_label,
np.argmax(attack_prob, 1), sr))
if arg_save_iteration:
os.system("mkdir Examples")
if (np.logical_or(
true_label != np.argmax(attack_prob, 1),
np.remainder(i + 1, 10) == 0)): ## every 10 iterations
suffix = "id_{}_Mode_{}_True_{}_Pred_{}_Ite_{}".format(
class_id, arg_mode, true_label,
np.argmax(attack_prob, 1), i + 1)
# util.save_img(adv_img, "Examples/{}.png".format(suffix))
if arg_targeted_attack:
if ((np.log(target_prob + 1e-10) - np.log(other_prob + 1e-10))
>= kappa).all(): # check attack confidence
if (distortion(adv_img, orig_img) <
best_distortion): # check distortion
# print('best distortion obtained at',i,'-th iteration')
best_adv_img = adv_img
best_distortion = distortion(adv_img, orig_img)
#best_delta = adv_img - orig_img
best_iteration = i + 1
adv_class = np.argmax(attack_prob, 1)
attack_flag = True
## Record first attack
if (first_flag):
first_flag = False ### once gets into this, it will no longer record the next sucessful attack
first_adv_img = adv_img
first_distortion = distortion(adv_img, orig_img)
#first_delta = adv_img - orig_img
first_class = adv_class
first_iteration = i + 1
else:
if ((np.log(other_prob + 1e-10) - np.log(target_prob + 1e-10))
>= kappa).all(): # check attack confidence
if (distortion(adv_img, orig_img) <
best_distortion): # check distortion
# print('best distortion obtained at',i,'-th iteration')
best_adv_img = adv_img
best_distortion = distortion(adv_img, orig_img)
#best_delta = adv_img - orig_img
best_iteration = i + 1
adv_class = np.argmax(attack_prob, 1)
attack_flag = True
## Record first attack
if (first_flag):
first_flag = False
first_adv_img = adv_img
first_distortion = distortion(adv_img, orig_img)
#first_delta = adv_img - orig_img
first_class = adv_class
first_iteration = i + 1
if (attack_flag):
# os.system("mkdir Results_SL")
# ## best attack (final attack)
# suffix = "id_{}_Mode_{}_True_{}_Pred_{}".format(class_id, arg_mode, true_label, orig_class) ## orig_class, predicted label
# suffix2 = "id_{}_Mode_{}_True_{}_Pred_{}".format(class_id, arg_mode, true_label, adv_class)
# suffix3 = "id_{}_Mode_{}".format(class_id, arg_mode)
# ### save original image
# util.save_img(orig_img, "Results_SL/id_{}.png".format(class_id))
# util.save_img(orig_img, "Results_SL/{}_Orig.png".format(suffix))
# ### adv. image
# util.save_img(best_adv_img, "Results_SL/{}_Adv_best.png".format(suffix2))
# ### adv. perturbation
# util.save_img(best_delta, "Results_SL/{}_Delta_best.png".format(suffix3))
#
#
# ## first attack
# suffix4 = "id_{}_Mode_{}_True_{}_Pred_{}".format(class_id, arg_mode, true_label, first_class)
# ## first adv. imag
# util.save_img(first_adv_img, "Results_SL/{}_Adv_first.png".format(suffix4))
# ### first adv. perturbation
# util.save_img(first_delta, "Results_SL/{}_Delta_first.png".format(suffix3))
## save data
suffix0 = "id_{}_Mode_{}_{}_lr_{}_decay_{}_case{}_ini_{}".format(
class_id, arg_mode, args["constraint"], str(args["lr"]),
int(args["decay_lr"]), args["exp_code"], args["init_const"])
np.savez("{}".format(suffix0),
id=class_id,
mode=arg_mode,
loss=total_loss,
perturbation=l2s_loss_all,
best_distortion=best_distortion,
first_distortion=first_distortion,
first_iteration=first_iteration,
best_iteation=best_iteration,
learn_rate=args["lr"],
decay_lr=args["decay_lr"],
attack_flag=attack_flag)
## print
print("It takes {} iteations to find the first attack".format(
first_iteration))
# print(total_loss)
else:
## save data
suffix0 = "id_{}_Mode_{}_{}_lr_{}_decay_{}_case{}_ini_{}".format(
class_id, arg_mode, args["constraint"], str(args["lr"]),
int(args["decay_lr"]), args["exp_code"], args["init_const"])
np.savez("{}".format(suffix0),
id=class_id,
mode=arg_mode,
loss=total_loss,
perturbation=l2s_loss_all,
best_distortion=best_distortion,
learn_rate=args["lr"],
decay_lr=args["decay_lr"],
attack_flag=attack_flag)
print("Attack Fails")
sys.stdout.flush()
#print(h%10)
#print(sess.run(model.predict(new_img)[h]))
#print(np.argmax(label_batch,1)[h])
#print(loss_instant[h])
#print(np.argmax(sess.run(model.predict(tf.tanh(img)*0.5+0.5))[h]))
#print(sess.run(attack_pixel[h]))
#label_acc.append(sum(np.argmax(sess.run(model.predict(new_img)),1)==np.argmax(label_batch,1))/90)
#label_disloss.append(sum(lowest_dist[lowest_dist<1000])/len(lowest_dist<1000))
#print(lowest_dist)
#print(label_disloss)
#print(label_acc)
#print(label_disloss)
print(sum(const_c) / 90, min(const_c), max(const_c))
#load data
train_data, train_label, test_data, test_label = load_data()
data_batch, label_batch = batch_loader(train_data, train_label)
#load model
with tf.Session() as sess:
model = MNISTModel('models/mnist', sess)
original_img = np.arctanh((data_batch - 0.5) * 2 * 0.999)
print(attack2(data_batch, label_batch, 90, model, sess))
