def test_lbfgs_attack():
"""
LBFGS-Attack test
"""
np.random.seed(123)
# upload trained network
current_dir = os.path.dirname(os.path.abspath(__file__))
ckpt_name = os.path.join(
current_dir,
'../test_data/trained_ckpt_file/checkpoint_lenet-10_1875.ckpt')
net = LeNet5()
load_dict = load_checkpoint(ckpt_name)
load_param_into_net(net, load_dict)
# get one mnist image
input_np = np.load(
os.path.join(current_dir, '../test_data/test_images.npy'))[:1]
label_np = np.load(
os.path.join(current_dir, '../test_data/test_labels.npy'))[:1]
LOGGER.debug(TAG, 'true label is :{}'.format(label_np[0]))
classes = 10
target_np = np.random.randint(0, classes, 1)
while target_np == label_np[0]:
target_np = np.random.randint(0, classes)
target_np = np.eye(10)[target_np].astype(np.float32)
attack = LBFGS(net, is_targeted=True)
LOGGER.debug(TAG, 'target_np is :{}'.format(target_np[0]))
adv_data = attack.generate(input_np, target_np)
def test_pointwise_attack_method():
"""
Pointwise attack method unit test.
"""
np.random.seed(123)
# upload trained network
current_dir = os.path.dirname(os.path.abspath(__file__))
ckpt_name = os.path.join(
current_dir,
'../../test_data/trained_ckpt_file/checkpoint_lenet-10_1875.ckpt')
net = LeNet5()
load_dict = load_checkpoint(ckpt_name)
load_param_into_net(net, load_dict)
# get one mnist image
input_np = np.load(
os.path.join(current_dir, '../../test_data/test_images.npy'))[:3]
labels = np.load(
os.path.join(current_dir, '../../test_data/test_labels.npy'))[:3]
model = ModelToBeAttacked(net)
pre_label = np.argmax(model.predict(input_np), axis=1)
LOGGER.info(TAG,
'original sample predict labels are :{}'.format(pre_label))
LOGGER.info(TAG, 'true labels are: {}'.format(labels))
attack = PointWiseAttack(model, sparse=True, is_targeted=False)
is_adv, adv_data, query_times = attack.generate(input_np, pre_label)
LOGGER.info(
TAG, 'adv sample predict labels are: {}'.format(
np.argmax(model.predict(adv_data), axis=1)))
assert np.any(adv_data[is_adv][0] != input_np[is_adv][0]), 'Pointwise attack method: ' \
'generate value must not be equal' \
' to original value.'
def get_model(current_dir):
ckpt_name = os.path.join(
current_dir,
'../../test_data/trained_ckpt_file/checkpoint_lenet-10_1875.ckpt')
net = LeNet5()
load_dict = load_checkpoint(ckpt_name)
load_param_into_net(net, load_dict)
net.set_train(False)
model = ModelToBeAttacked(net)
return model