def main(argv=None):
datasets = FLAGS.datasets
start=FLAGS.start
end=FLAGS.end
if 'mnist' == datasets:
train_start = 0
train_end = 60000
test_start = 0
test_end = 10000
# Get MNIST test data
X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end)
sample = X_test[start:end]
#imsave(FLAGS.sample, deprocess_image_1(sample))
elif 'cifar10' == datasets:
preprocess_image = preprocess_image_1
train_start = 0
train_end = 50000
test_start = 0
test_end = 10000
# Get CIFAR10 test data
X_train, Y_train, X_test, Y_test = data_cifar10(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end,
preprocess=preprocess_image)
sample = X_train[0:10000]
#imsave(FLAGS.sample, deprocess_image_1(sample))
elif 'svhn' == datasets:
# choose the method of preprocess image
preprocess_image = preprocess_image_1
train_start = 0
train_end = 73257
test_start = 0
test_end = 26032
# Get SVHN test data
X_train, Y_train, X_test, Y_test = data_svhn(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end,
preprocess=preprocess_image)
sample = X_test[0:100]
imsave(FLAGS.sample, deprocess_image_1(sample))
store_path = 'test'
suc,total=cw(datasets=datasets,
sample=sample,
model_name=FLAGS.model,
target=FLAGS.target,
store_path=store_path,
ini_con=0.1,start=start,end=end)
print(suc)
print(total)
def main(argv=None):
datasets = FLAGS.datasets
if 'mnist' == datasets:
train_start = 0
train_end = 60000
test_start = 0
test_end = 10000
# Get MNIST test data
X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end)
sample = X_test[0:1000]
elif 'cifar10' == datasets:
preprocess_image = preprocess_image_1
train_start = 0
train_end = 50000
test_start = 0
test_end = 10000
# Get CIFAR10 test data
X_train, Y_train, fn_train, X_test, Y_test, fn_test = data_cifar10(
train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end,
preprocess=preprocess_image)
sample = X_test[198:199]
imsave(FLAGS.sample, deprocess_image_1(sample))
elif 'svhn' == datasets:
# choose the method of preprocess image
preprocess_image = preprocess_image_1
train_start = 0
train_end = 73257
test_start = 0
test_end = 26032
# Get SVHN test data
X_train, Y_train, X_test, Y_test = data_svhn(
train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end,
preprocess=preprocess_image)
sample = X_test[198:199]
imsave(FLAGS.sample, deprocess_image_1(sample))
store_path = '../datasets/experiment/mnist/fgsm/test'
fgsm(datasets=datasets,
sample=sample,
model_name=FLAGS.model_name,
store_path=store_path,
step_size=FLAGS.step_size,
epoch=FLAGS.epoch)
def main(argv=None):
datasets = FLAGS.datasets
if 'mnist' == datasets:
train_start = 0
train_end = 60000
test_start = 0
test_end = 10000
# Get MNIST test data
X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end)
sample = X_test[0:10]
elif 'cifar10' == datasets:
preprocess_image = preprocess_image_1
train_start = 0
train_end = 50000
test_start = 0
test_end = 10000
# Get CIFAR10 test data
X_train, Y_train, X_test, Y_test = data_cifar10(
train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end,
preprocess=preprocess_image)
sample = X_test[0:2]
#imsave('a.png', deprocess_image_3(sample))
elif 'svhn' == datasets:
# choose the method of preprocess image
preprocess_image = preprocess_image_1
train_start = 0
train_end = 73257
test_start = 0
test_end = 26032
# Get SVHN test data
X_train, Y_train, X_test, Y_test = data_svhn(
train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end,
preprocess=preprocess_image)
sample = X_test[198:199]
imsave(FLAGS.sample, deprocess_image_1(sample))
store_path = 'test0.03'
bim(datasets=datasets,
sample=sample,
model_name=FLAGS.model,
store_path=store_path,
step_size='0.03')
def main(argv=None):
datasets = FLAGS.datasets
if 'mnist' == datasets:
train_start = 0
train_end = 60000
test_start = 0
test_end = 10000
# Get MNIST test data
X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end)
sample = X_test[0:1]
imsave(FLAGS.sample, deprocess_image_1(sample))
elif 'cifar10' == datasets:
preprocess_image = preprocess_image_1
train_start = 0
train_end = 50000
test_start = 0
test_end = 10000
# Get CIFAR10 test data
X_train, Y_train, X_test, Y_test = data_cifar10(
train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end,
preprocess=preprocess_image)
sample = X_test[198:199]
imsave(FLAGS.sample, deprocess_image_1(sample))
elif 'svhn' == datasets:
# choose the method of preprocess image
preprocess_image = preprocess_image_1
train_start = 0
train_end = 73257
test_start = 0
test_end = 26032
# Get SVHN test data
X_train, Y_train, X_test, Y_test = data_svhn(
train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end,
preprocess=preprocess_image)
sample = X_test[198:199]
imsave(FLAGS.sample, deprocess_image_1(sample))
jsma(datasets=FLAGS.datasets,
sample_path=FLAGS.sample,
model_name=FLAGS.model,
target=FLAGS.target,
store_path=FLAGS.store_path)
def get_data(datasets):
if 'mnist' == datasets:
train_start = 0
train_end = 60000
test_start = 0
test_end = 10000
# Get MNIST test data
X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end)
elif 'cifar10' == datasets:
# choose the method of preprocess image
preprocess_image = preprocess_image_1
train_start = 0
train_end = 50000
test_start = 0
test_end = 10000
# Get CIFAR10 test data
X_train, Y_train, X_test, Y_test = data_cifar10(
train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end,
preprocess=preprocess_image)
elif 'svhn' == datasets:
# choose the method of preprocess image
preprocess_image = preprocess_image_1
train_start = 0
train_end = 73257
test_start = 0
test_end = 26032
# Get SVHN test data
X_train, Y_train, X_test, Y_test = data_svhn(
train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end,
preprocess=preprocess_image)
return X_train, Y_train, X_test, Y_test
def mutation_tutorial(datasets,
attack,
sample_path,
store_path,
model_path,
level=1,
test_num=100,
mutation_number=1000,
mutated=False):
if 'mnist' == datasets:
train_start = 0
train_end = 60000
test_start = 0
test_end = 10000
# Get MNIST test data
X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end)
elif 'cifar10' == datasets:
preprocess_image = preprocess_image_1
train_start = 0
train_end = 50000
test_start = 0
test_end = 10000
# Get CIFAR10 test data
X_train, Y_train, fn_train, X_test, Y_test, fn_test = data_cifar10(
train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end,
preprocess=preprocess_image)
elif 'svhn' == datasets:
# choose the method of preprocess image
preprocess_image = preprocess_image_1
train_start = 0
train_end = 73257
test_start = 0
test_end = 26032
# Get SVHN test data
X_train, Y_train, X_test, Y_test = data_svhn(
train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end,
preprocess=preprocess_image)
sess, preds, x, y, model, feed_dict = model_load(datasets,
model_path + datasets)
# Generate random matution matrix for mutations
store_path = store_path + attack + '/' + datasets + '/' + str(level)
if not os.path.exists(store_path):
os.makedirs(store_path)
result = ''
sample_path = sample_path + attack + '/' + datasets
[image_list, image_files, real_labels,
predicted_labels] = utils.get_data_mutation_test(sample_path)
index = np.random.choice(len(image_files), test_num, replace=False)
image_list = np.asarray(image_list)[index]
image_files = np.asarray(image_files)[index].tolist()
predicted_labels = np.asarray(predicted_labels)[index].tolist()
seed_number = len(image_list)
if datasets == 'mnist':
img_rows = 28
img_cols = 28
mutation_test = MutationTest(img_rows, img_cols, seed_number,
mutation_number, level)
mutation_test.mutation_generate(mutated, store_path,
utils.generate_value_1)
elif datasets == 'cifar10' or datasets == 'svhn':
img_rows = 32
img_cols = 32
mutation_test = MutationTest(img_rows, img_cols, seed_number,
mutation_number, level)
mutation_test.mutation_generate(mutated, store_path,
utils.generate_value_3)
store_string, result = mutation_test.mutation_test_adv(
preprocess_image_1, result, image_list, predicted_labels, sess, x,
preds, image_files, feed_dict)
with open(store_path + "/adv_result.csv", "w") as f:
f.write(store_string)
path = store_path + '/ori_jsma'
if not os.path.exists(path):
os.makedirs(path)
preds_test = np.asarray([])
for i in range(40):
preds_test = np.concatenate(
(preds_test,
model_argmax(sess,
x,
preds,
X_test[i * 250:(i + 1) * 250],
feed=feed_dict)))
inds_correct = np.asarray(np.where(preds_test == Y_test.argmax(axis=1))[0])
inds_correct = inds_correct[np.random.choice(len(inds_correct),
test_num,
replace=False)]
image_list = X_test[inds_correct]
real_labels = Y_test[inds_correct].argmax(axis=1)
np.save(path + '/ori_x.npy', np.asarray(image_list))
np.save(path + '/ori_y.npy', np.asarray(real_labels))
image_list = np.load(path + '/ori_x.npy')
real_labels = np.load(path + '/ori_y.npy')
store_string, result = mutation_test.mutation_test_ori(
result, image_list, sess, x, preds, feed_dict)
with open(store_path + "/ori_result.csv", "w") as f:
f.write(store_string)
with open(store_path + "/result.csv", "w") as f:
f.write(result)
# Close TF session
sess.close()
print('Finish.')
def batch_attack(datasets, attack, model_path, store_path, nb_classes):
if 'mnist' == datasets:
train_start = 0
train_end = 60000
test_start = 0
test_end = 10000
# Get MNIST test data
X_train, Y_train, X_test, Y_test = data_mnist(train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end)
elif 'cifar10' == datasets:
preprocess_image = preprocess_image_1
train_start = 0
train_end = 50000
test_start = 0
test_end = 10000
# Get CIFAR10 test data
X_train, Y_train, fn_train, X_test, Y_test, fn_test = data_cifar10(
train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end,
preprocess=preprocess_image)
elif 'svhn' == datasets:
# choose the method of preprocess image
preprocess_image = preprocess_image_1
train_start = 0
train_end = 73257
test_start = 0
test_end = 26032
# Get SVHN test data
X_train, Y_train, X_test, Y_test = data_svhn(
train_start=train_start,
train_end=train_end,
test_start=test_start,
test_end=test_end,
preprocess=preprocess_image)
store_path = store_path + attack + '/' + datasets
sample_path = '../datasets/integration/batch_attack/' + datasets + '/'
sess, preds, x, y, model, feed_dict = model_load(datasets, model_path)
if os.listdir(sample_path) == []:
for i in range(len(X_test)):
sample = X_test[i:i + 1]
path = sample_path + str(i) + '.png'
imsave(path, deprocess_image_1(sample))
current_img = ndimage.imread(path)
img = np.expand_dims(
preprocess_image_1(current_img.astype('float64')), 0)
p = model_argmax(sess, x, preds, img, feed=feed_dict)
if p != Y_test[i].argmax(axis=0):
os.remove(path)
# for i in range(len(X_test)):
# sample = X_test[i:i+1]
# if model_argmax(sess, x, preds, sample, feed=feed_dict) == Y_test[i].argmax(axis=0):
# path = sample_path + str(i) + '.png'
# imsave(path, deprocess_image_1(sample))
sess.close()
samples = os.listdir(sample_path)
for sample in samples:
tf.reset_default_graph()
if 'blackbox' == attack:
blackbox(datasets=datasets,
sample_path=sample_path + sample,
model_path=model_path,
store_path=store_path,
nb_classes=nb_classes)
elif 'fgsm' == attack:
fgsm(datasets=datasets,
sample_path=sample_path + sample,
model_path=model_path,
store_path=store_path,
nb_classes=nb_classes)
else:
i = int(sample.split('.')[-2])
for j in range(nb_classes):
tf.reset_default_graph()
if Y_test[i][j] == 0:
if 'jsma' == attack:
jsma(datasets=datasets,
sample_path=sample_path + sample,
target=j,
model_path=model_path,
store_path=store_path,
nb_classes=nb_classes)
if 'cw' == attack:
cw(datasets=datasets,
sample_path=sample_path + sample,
target=j,
model_path=model_path,
store_path=store_path,
nb_classes=nb_classes)