def attack():
x_train, x_test, y_train, y_test = load_data('cifar10', 2)
min_pixel_value = x_train.min()
max_pixel_value = x_train.max()
print('min_pixel_value ', min_pixel_value)
print('max_pixel_value ', max_pixel_value)
s = time.time()
# model = BNN(['../binary/checkpoints/cifar10_mlpbnn_approx_%d.h5' % (i) for i in range(100)])
model = BNN([
'../binary/checkpoints/cifar10_mlpbnn_approx_ep004_%d.h5' % (i)
for i in range(100)
])
pred_y = model.predict(x_test)
print('pred_y: ', pred_y)
np.savetxt('pred_y', pred_y)
np.savetxt('y_test', y_test)
print('pred_y[0], pred_y[288], pred_y[888], pred_y[1990], y[-1]',
pred_y[0], pred_y[288], pred_y[888], pred_y[1990], y[-1])
print('Accuracy: ', accuracy_score(y_true=y_test, y_pred=pred_y))
# Create a model wrapper
predictWrapper = modelWrapper(model)
classifier = BlackBoxClassifier(predict=predictWrapper.predict_one_hot,
input_shape=(32 * 32 * 3, ),
nb_classes=2,
clip_values=(min_pixel_value,
max_pixel_value))
print('----- generate adv data -----')
attack = BoundaryAttack(estimator=classifier,
targeted=False,
delta=0.01,
epsilon=0.01,
max_iter=100,
num_trial=100,
sample_size=100,
init_size=100)
print('----- generate adv test data -----')
x_test = x_test[288]
# Input data shape should be 2D
x_test = x_test.reshape((-1, 32 * 32 * 3))
x_test_adv = attack.generate(x=x_test)
print('x_test ', x_test)
print('x_test_adv ', x_test_adv)
dist2 = utils.computeDist2(x_test, x_test_adv)
print('test data dist2: ', dist2)
distInf = utils.computeDistInf(x_test, x_test_adv)
print('test data distInf: ', distInf)
print('Cost time: ', time.time() - s)
def adv_gen(attack, orginal_data, datatype):
adv_data = attack.generate(x=orginal_data)
print('x ', orginal_data)
print('x_adv ', adv_data)
dist2 = utils.computeDist2(orginal_data, adv_data)
print('test data dist2: ', dist2)
distInf = utils.computeDistInf(orginal_data, adv_data)
print('test data distInf: ', distInf)
def distortion(original_data, adv_data):
'''
Compute the distance between original data and adversary data
'''
print('x ', original_data)
print('x_adv ', adv_data)
dist2 = utils.computeDist2(original_data, adv_data)
print('test data dist2: ', dist2)
distInf = utils.computeDistInf(original_data, adv_data)
print('test data distInf: ', distInf)
def attack():
x_train, x_test, y_train, y_test = load_data('cifar10_binary', 2)
x_train = x_train.reshape((-1, 32, 32, 3)).transpose(
(0, 3, 1, 2)).astype(np.float32)
x_test = x_test.reshape((-1, 32, 32, 3)).transpose(
(0, 3, 1, 2)).astype(np.float32)
min_pixel_value = x_train.min()
max_pixel_value = x_train.max()
print('min_pixel_value ', min_pixel_value)
print('max_pixel_value ', max_pixel_value)
s = time.time()
path = '../binary/checkpoints/cifar10_binary_lenet_100.pkl'
with open(path, 'rb') as f:
model = pickle.load(f)
# Predict
# Lent and simpleNet input data shape is (-1,3, 32, 32)
# The other net input data shape is vector
# Mlp01 need to add: cuda=False
print('xtest shape1', x_test.shape)
pred_y = model.predict(x_test)
print('xtest shape1', x_test.shape)
print('pred_y: ', pred_y)
# Create a model wrapper
predictWrapper = modelWrapper(model)
classifier = BlackBoxClassifier(predict=predictWrapper.predict_one_hot,
input_shape=(3 * 32 * 32, ),
nb_classes=2,
clip_values=(min_pixel_value,
max_pixel_value))
print('----- generate adv data -----')
attack = BoundaryAttack(estimator=classifier,
targeted=False,
delta=0.01,
epsilon=0.01,
max_iter=100,
num_trial=100,
sample_size=100,
init_size=100)
print('----- generate adv test data -----')
x_test = x_test[288]
# Input data shape should be 2D
x_test = x_test.reshape((-1, 3 * 32 * 32))
x_test_adv = attack.generate(x=x_test)
np.save('x', x_test)
np.save('adv_x', x_test_adv)
print('x_test ', x_test)
print('x_test_adv ', x_test_adv)
# dist1 = utils.computeDist1(x_test, x_test_adv)
# print('test data dist1: ', dist1)
dist2 = utils.computeDist2(x_test, x_test_adv)
print('test data dist2: ', dist2)
distInf = utils.computeDistInf(x_test, x_test_adv)
print('test data distInf: ', distInf)
# avg_dist2, med_dist2 = utils.computeDist2(x_test, x_test_adv)
# print('test avg_dist2: ', avg_dist2)
# # print('test med_dist2: ', med_dist2)
# avg_distInf, med_distInf = utils.computeDistInf(x_test, x_test_adv)
# print('test avg_distInf: ', avg_distInf)
# # print('test med_distInf: ', med_distInf)
print('Cost time: ', time.time() - s)
def attack():
x_train, x_test, y_train, y_test = load_data('cifar10_binary', 2)
min_pixel_value = x_train.min()
max_pixel_value = x_train.max()
print('min_pixel_value ', min_pixel_value)
print('max_pixel_value ', max_pixel_value)
s = time.time()
path = '../binary/checkpoints/cifar10_binary_scd01mlp_100_br02_h500_nr075_ni25000_i1.pkl'
with open(path, 'rb') as f:
model = pickle.load(f)
pred_y = model.predict(x_test, cuda=False)
# np.savetxt('pred_y_mpl2', pred_y)
print('pred_y: ', pred_y)
# Create a model wrapper
predictWrapper = modelWrapper(model)
classifier = BlackBoxClassifier(predict=predictWrapper.predict_one_hot,
input_shape=(3 * 32 * 32, ),
nb_classes=2,
clip_values=(min_pixel_value,
max_pixel_value))
print('----- generate adv data -----')
attack = BoundaryAttack(estimator=classifier,
targeted=False,
delta=0.01,
epsilon=0.01,
max_iter=100,
num_trial=100,
sample_size=100,
init_size=100)
print('----- generate adv test data -----')
x_test = x_test[-1]
# Input data shape should be 2D
x_test = x_test.reshape((-1, 3 * 32 * 32))
x_test_adv = attack.generate(x=x_test)
np.save('x', x_test)
np.save('adv_x', x_test_adv)
print('x_test ', x_test)
print('x_test_adv ', x_test_adv)
# dist1 = utils.computeDist1(x_test, x_test_adv)
# print('test data dist1: ', dist1)
dist2 = utils.computeDist2(x_test, x_test_adv)
print('test data dist2: ', dist2)
distInf = utils.computeDistInf(x_test, x_test_adv)
print('test data distInf: ', distInf)
# avg_dist2, med_dist2 = utils.computeDist2(x_test, x_test_adv)
# print('test avg_dist2: ', avg_dist2)
# # print('test med_dist2: ', med_dist2)
# avg_distInf, med_distInf = utils.computeDistInf(x_test, x_test_adv)
# print('test avg_distInf: ', avg_distInf)
# # print('test med_distInf: ', med_distInf)
print('Cost time: ', time.time() - s)
def attack():
x_train, x_test, y_train, y_test = load_data('cifar10', 2)
# x_train, y_train, x_test, y_test = utils.loadData()
min_pixel_value = x_train.min()
max_pixel_value = x_train.max()
print('min_pixel_value ', min_pixel_value)
print('max_pixel_value ', max_pixel_value)
s = time.time()
path = '../binary/checkpoints/cifar10_scd01mlp_100_br02_nr075_ni1000_i1_ep2.pkl'
# path = '/home/y/yx277/research/scd01mc/binary/checkpoints/cifar10_mlp.pkl'
# path = '/research/datasci/mx42/adversarial_machine_learning/IBM_ART/checkpoints_scd_01/scd_stl10_01_v7.pkl'
with open(path, 'rb') as f:
model = pickle.load(f)
pred_y = model.predict(x_test)
# np.savetxt('pred_y_mpl2', pred_y)
print('pred_y: ', pred_y)
# Create a model wrapper
predictWrapper = modelWrapper(model)
classifier = BlackBoxClassifier(predict=predictWrapper.predict_one_hot,
input_shape=(32 * 32 * 3, ),
nb_classes=2,
clip_values=(min_pixel_value,
max_pixel_value))
print('----- generate adv data -----')
attack = BoundaryAttack(estimator=classifier,
targeted=False,
delta=0.01,
epsilon=0.01,
max_iter=500,
num_trial=100,
sample_size=100,
init_size=100)
print('----- generate adv test data -----')
x_test = x_test[0]
# Input data shape should be 2D
x_test = x_test.reshape((-1, 32 * 32 * 3))
x_test_adv = attack.generate(x=x_test)
np.save('x', x_test)
np.save('adv_x', x_test_adv)
print('x_test ', x_test)
print('x_test_adv ', x_test_adv)
# dist1 = utils.computeDist1(x_test, x_test_adv)
# print('test data dist1: ', dist1)
dist2 = utils.computeDist2(x_test, x_test_adv)
print('test data dist2: ', dist2)
distInf = utils.computeDistInf(x_test, x_test_adv)
print('test data distInf: ', distInf)
# avg_dist2, med_dist2 = utils.computeDist2(x_test, x_test_adv)
# print('test avg_dist2: ', avg_dist2)
# # print('test med_dist2: ', med_dist2)
# avg_distInf, med_distInf = utils.computeDistInf(x_test, x_test_adv)
# print('test avg_distInf: ', avg_distInf)
# # print('test med_distInf: ', med_distInf)
print('Cost time: ', time.time() - s)
