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 detect_adv_samples(datasets, model_path, sample_path, store_path,
attack_type):
print('Loading the data and model...')
# Load the model
sess, preds, x, y, model, feed_dict = model_load(datasets, model_path)
# # Load the dataset
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)
# # Refine the normal, noisy and adversarial sets to only include samples for
# # which the original version was correctly classified by the model
# preds_test = model_argmax(sess, x, preds, X_test, feed=feed_dict)
# inds_correct = np.where(preds_test == Y_test.argmax(axis=1))[0]
# X_test = X_test[inds_correct]
# X_test = X_test[np.random.choice(len(X_test), 500)]#500
#
# # Check attack type, select adversarial and noisy samples accordingly
# print('Loading adversarial samples...')
# # Load adversarial samplesx
# [X_test_adv, adv_image_files, real_labels, predicted_labels] = utils.get_data_mutation_test(sample_path)
# X_test_adv = preprocess_image_1(np.asarray(X_test_adv).astype('float32'))
# if len(X_test_adv.shape) < 4:
# X_test_adv = np.expand_dims(X_test_adv, axis=3)
[X_test_adv_train, adv_image_files, real_labels, predicted_labels
] = utils.get_data_mutation_test("../datasets/experiment/" + datasets +
"/" + attack_type + "/train")
[X_test_adv_test, adv_image_files, real_labels, predicted_labels
] = utils.get_data_mutation_test("../datasets/experiment/" + datasets +
"/" + attack_type + "/test")
train_num = len(X_test_adv_train)
test_num = len(X_test_adv_test)
X_test_adv = preprocess_image_1(
np.concatenate((np.asarray(X_test_adv_train),
np.asarray(X_test_adv_test))).astype('float32'))
if len(X_test_adv.shape) < 4:
X_test_adv = np.expand_dims(X_test_adv, axis=3)
[X_test_train, adv_image_files, real_labels,
predicted_labels] = utils.get_data_normal_test("../datasets/experiment/" +
datasets + "/normal/train")
[X_test_test, adv_image_files, real_labels,
predicted_labels] = utils.get_data_normal_test("../datasets/experiment/" +
datasets + "/normal/test")
X_test_train = np.asarray(X_test_train)[np.random.choice(len(X_test_train),
train_num,
replace=False)]
X_test_test = np.asarray(X_test_test)[np.random.choice(len(X_test_test),
test_num,
replace=False)]
X_test = preprocess_image_1(
np.concatenate((np.asarray(X_test_train),
np.asarray(X_test_test))).astype('float32'))
if len(X_test.shape) < 4:
X_test = np.expand_dims(X_test, axis=3)
## Get Bayesian uncertainty scores
print('Getting Monte Carlo dropout variance predictions...')
uncerts_normal = get_mc_predictions(sess, x, preds,
X_test).var(axis=0).mean(axis=1)
uncerts_adv = get_mc_predictions(sess, x, preds,
X_test_adv).var(axis=0).mean(axis=1)
## Get KDE scores
# Get deep feature representations
print('Getting deep feature representations...')
X_train_features = get_deep_representations(sess, x, X_train, model,
feed_dict)
X_test_normal_features = get_deep_representations(sess, x, X_test, model,
feed_dict)
X_test_adv_features = get_deep_representations(sess, x, X_test_adv, model,
feed_dict)
# Train one KDE per class
print('Training KDEs...')
class_inds = {}
for i in range(Y_train.shape[1]):
class_inds[i] = np.where(Y_train.argmax(axis=1) == i)[0]
kdes = {}
warnings.warn(
"Using pre-set kernel bandwidths that were determined "
"optimal for the specific CNN models of the paper. If you've "
"changed your model, you'll need to re-optimize the "
"bandwidth.")
for i in range(Y_train.shape[1]):
kdes[i] = KernelDensity(kernel='gaussian',
bandwidth=BANDWIDTHS[datasets]) \
.fit(X_train_features[class_inds[i]])
# Get model predictions
print('Computing model predictions...')
preds_test_normal = model_argmax(sess, x, preds, X_test, feed=feed_dict)
preds_test_adv = model_argmax(sess, x, preds, X_test_adv, feed=feed_dict)
# Get density estimates
print('computing densities...')
densities_normal = score_samples(kdes, X_test_normal_features,
preds_test_normal)
densities_adv = score_samples(kdes, X_test_adv_features, preds_test_adv)
uncerts_pos = uncerts_adv[:]
uncerts_neg = uncerts_normal[:]
characteristics, labels = merge_and_generate_labels(
uncerts_pos, uncerts_neg)
file_name = os.path.join('../detection/bu/',
"%s_%s.npy" % (datasets, attack_type))
data = np.concatenate((characteristics, labels), axis=1)
np.save(file_name, data)
densities_pos = densities_adv[:]
densities_neg = densities_normal[:]
characteristics, labels = merge_and_generate_labels(
densities_pos, densities_neg)
file_name = os.path.join(
'../detection/de/',
"%s_%s_%.4f.npy" % (datasets, attack_type, BANDWIDTHS[datasets]))
data = np.concatenate((characteristics, labels), axis=1)
np.save(file_name, data)
## Z-score the uncertainty and density values
uncerts_normal_z, uncerts_adv_z = normalize(uncerts_normal, uncerts_adv)
densities_normal_z, densities_adv_z = normalize(densities_normal,
densities_adv)
## Build detector
values, labels = features(densities_pos=densities_adv_z,
densities_neg=densities_normal_z,
uncerts_pos=uncerts_adv_z,
uncerts_neg=uncerts_normal_z)
X_tr, Y_tr, X_te, Y_te = block_split(values, labels, train_num)
lr = train_lr(X_tr, Y_tr)
## Evaluate detector
# Compute logistic regression model predictions
probs = lr.predict_proba(X_te)[:, 1]
preds = lr.predict(X_te)
# Compute AUC
n_samples = int(len(X_te) / 2)
# The first 2/3 of 'probs' is the negative class (normal and noisy samples),
# and the last 1/3 is the positive class (adversarial samples).
_, _, auc_score = compute_roc(probs_neg=probs[:n_samples],
probs_pos=probs[n_samples:])
precision = precision_score(Y_te, preds)
recall = recall_score(Y_te, preds)
y_label_pred = lr.predict(X_te)
acc = accuracy_score(Y_te, y_label_pred)
print(
'Detector ROC-AUC score: %0.4f, accuracy: %.4f, precision: %.4f, recall: %.4f'
% (auc_score, acc, precision, recall))
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)
from nmutant_data.cifar10 import data_cifar10
import tensorflow as tf
from nmutant_data.data import get_shape
from nmutant_util.utils_imgproc import deprocess_image_1, preprocess_image_1, deprocess_image_1
datasets = 'cifar10'
num = 10000
train_start = 0
train_end = 50000
test_start = 0
test_end = 10000
preprocess_image = preprocess_image_1
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)
input_shape, nb_classes = get_shape(datasets)
sample = X_test[0:num]
models3 = ['lenet1', 'lenet4', 'lenet5']
models2 = ['vgg11', 'vgg13', 'vgg16', 'vgg19']
models1 = [
'resnet18', 'resnet34', 'resnet50', 'resnet101', 'resnet152',
'googlenet12', 'googlenet16', 'googlenet22'
]
models = [
'vgg11', 'vgg13', 'vgg16', 'vgg19', 'resnet18', 'resnet34', 'resnet50',
'resnet101', 'resnet152', 'googlenet12', 'googlenet16', 'googlenet22'
]
def prepare_datasets(datasets, model_path, attack_type, sample_path):
print('Loading the data and model...')
# Load the model
sess, preds, x, y, model, feed_dict = model_load(datasets, model_path)
# Load the dataset
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)
if attack_type == "normal":
# Refine the normal, noisy and adversarial sets to only include samples for
# which the original version was correctly classified by the model
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),
5000,
replace=False)]
X_test = X_test[inds_correct]
for i in range(4000):
imsave(
"../datasets/experiment/" + datasets + "/normal/train/" +
str(inds_correct[i]) + '_' +
str(int(preds_test[inds_correct[i]])) + '_' +
str(int(preds_test[inds_correct[i]])) + '_.png',
deprocess_image_1(X_test[i:i + 1]))
for j in range(1000):
imsave(
"../datasets/experiment/" + datasets + "/normal/test/" +
str(inds_correct[4000 + j]) + '_' +
str(int(preds_test[inds_correct[4000 + j]])) + '_' +
str(int(preds_test[inds_correct[4000 + j]])) + '_.png',
deprocess_image_1(X_test[4000 + j:4001 + j]))
elif attack_type == "error":
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])
X_test = X_test[inds_correct]
num = int(len(X_test) * 0.8)
for i in range(num):
imsave(
"../datasets/experiment/" + datasets + "/error/train/" +
str(inds_correct[i]) + '_' +
str(int(np.argmax(Y_test[inds_correct[i]]))) + '_' +
str(int(preds_test[inds_correct[i]])) + '_.png',
deprocess_image_1(X_test[i:i + 1]))
for j in range(len(X_test) - num):
imsave(
"../datasets/experiment/" + datasets + "/error/test/" +
str(inds_correct[num + j]) + '_' +
str(int(np.argmax(Y_test[inds_correct[num + j]]))) + '_' +
str(int(preds_test[inds_correct[num + j]])) + '_.png',
deprocess_image_1(X_test[num + j:num + 1 + j]))
else:
# Check attack type, select adversarial and noisy samples accordingly
print('Loading adversarial samples...')
# Load adversarial samplesx
[X_test_adv, adv_image_files, real_labels, predicted_labels
] = utils.get_data_mutation_test(sample_path + attack_type + '/' +
datasets)
if len(X_test_adv) > 5000:
index = np.asarray(range(len(X_test_adv)))
index = index[np.random.choice(len(index), 5000, replace=False)]
for i in range(4000):
imsave(
"../datasets/experiment/" + datasets + "/" + attack_type +
"/train/" + adv_image_files[index[i]],
X_test_adv[index[i]])
for j in range(1000):
imsave(
"../datasets/experiment/" + datasets + "/" + attack_type +
"/test/" + adv_image_files[index[4000 + j]],
X_test_adv[index[4000 + j]])
else:
index = np.asarray(range(len(X_test_adv)))
np.random.shuffle(index)
cut = int(len(X_test_adv) * 0.8)
for i in range(len(index)):
if i < cut:
imsave(
"../datasets/experiment/" + datasets + "/" +
attack_type + "/train/" + adv_image_files[index[i]],
X_test_adv[index[i]])
else:
imsave(
"../datasets/experiment/" + datasets + "/" +
attack_type + "/test/" + adv_image_files[index[i]],
X_test_adv[index[i]])
