def test_fast_gradient_sign_method():
"""
Fast gradient sign method unit test.
"""
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
input_np = np.asarray([[0.1, 0.2, 0.7]], np.float32)
label = np.asarray([2], np.int32)
label = np.eye(3)[label].astype(np.float32)
attack = FastGradientSignMethod(Net())
ms_adv_x = attack.generate(input_np, label)
assert np.any(ms_adv_x != input_np), 'Fast gradient sign method: generate' \
' value must not be equal to' \
' original value.'
def test_defense_evaluation():
# load trained network
current_dir = os.path.dirname(os.path.abspath(__file__))
ckpt_path = os.path.abspath(
os.path.join(
current_dir,
'../../common/networks/lenet5/trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
))
wb_net = LeNet5()
load_dict = load_checkpoint(ckpt_path)
load_param_into_net(wb_net, load_dict)
# get test data
data_list = "../../common/dataset/MNIST/test"
batch_size = 32
ds_test = generate_mnist_dataset(data_list, batch_size=batch_size)
inputs = []
labels = []
for data in ds_test.create_tuple_iterator(output_numpy=True):
inputs.append(data[0].astype(np.float32))
labels.append(data[1])
inputs = np.concatenate(inputs).astype(np.float32)
labels = np.concatenate(labels).astype(np.int32)
target_label = np.random.randint(0, 10, size=labels.shape[0])
for idx in range(labels.shape[0]):
while target_label[idx] == labels[idx]:
target_label[idx] = np.random.randint(0, 10)
target_label = np.eye(10)[target_label].astype(np.float32)
attacked_size = 50
benign_size = 500
attacked_sample = inputs[:attacked_size]
attacked_true_label = labels[:attacked_size]
benign_sample = inputs[attacked_size:attacked_size + benign_size]
wb_model = ModelToBeAttacked(wb_net)
# gen white-box adversarial examples of test data
loss = SoftmaxCrossEntropyWithLogits(sparse=True)
wb_attack = FastGradientSignMethod(wb_net, eps=0.3, loss_fn=loss)
wb_adv_sample = wb_attack.generate(attacked_sample, attacked_true_label)
wb_raw_preds = softmax(wb_model.predict(wb_adv_sample), axis=1)
accuracy_test = np.mean(
np.equal(np.argmax(wb_model.predict(attacked_sample), axis=1),
attacked_true_label))
LOGGER.info(TAG, "prediction accuracy before white-box attack is : %s",
accuracy_test)
accuracy_adv = np.mean(
np.equal(np.argmax(wb_raw_preds, axis=1), attacked_true_label))
LOGGER.info(TAG, "prediction accuracy after white-box attack is : %s",
accuracy_adv)
# improve the robustness of model with white-box adversarial examples
opt = nn.Momentum(wb_net.trainable_params(), 0.01, 0.09)
nad = NaturalAdversarialDefense(wb_net,
loss_fn=loss,
optimizer=opt,
bounds=(0.0, 1.0),
eps=0.3)
wb_net.set_train(False)
nad.batch_defense(inputs[:5000], labels[:5000], batch_size=32, epochs=10)
wb_def_preds = wb_net(Tensor(wb_adv_sample)).asnumpy()
wb_def_preds = softmax(wb_def_preds, axis=1)
accuracy_def = np.mean(
np.equal(np.argmax(wb_def_preds, axis=1), attacked_true_label))
LOGGER.info(TAG, "prediction accuracy after defense is : %s", accuracy_def)
# calculate defense evaluation metrics for defense against white-box attack
wb_def_evaluate = DefenseEvaluate(wb_raw_preds, wb_def_preds,
attacked_true_label)
LOGGER.info(TAG, 'defense evaluation for white-box adversarial attack')
LOGGER.info(
TAG, 'classification accuracy variance (CAV) is : {:.2f}'.format(
wb_def_evaluate.cav()))
LOGGER.info(
TAG, 'classification rectify ratio (CRR) is : {:.2f}'.format(
wb_def_evaluate.crr()))
LOGGER.info(
TAG, 'classification sacrifice ratio (CSR) is : {:.2f}'.format(
wb_def_evaluate.csr()))
LOGGER.info(
TAG, 'classification confidence variance (CCV) is : {:.2f}'.format(
wb_def_evaluate.ccv()))
LOGGER.info(
TAG, 'classification output stability is : {:.2f}'.format(
wb_def_evaluate.cos()))
# calculate defense evaluation metrics for defense against black-box attack
LOGGER.info(TAG, 'defense evaluation for black-box adversarial attack')
bb_raw_preds = []
bb_def_preds = []
raw_query_counts = []
raw_query_time = []
def_query_counts = []
def_query_time = []
def_detection_counts = []
# gen black-box adversarial examples of test data
bb_net = LeNet5()
load_param_into_net(bb_net, load_dict)
bb_model = ModelToBeAttacked(bb_net, defense=False)
attack_rm = GeneticAttack(model=bb_model,
pop_size=6,
mutation_rate=0.05,
per_bounds=0.5,
step_size=0.25,
temp=0.1,
sparse=False)
attack_target_label = target_label[:attacked_size]
true_label = labels[:attacked_size + benign_size]
# evaluate robustness of original model
# gen black-box adversarial examples of test data
for idx in range(attacked_size):
raw_st = time.time()
_, raw_a, raw_qc = attack_rm.generate(
np.expand_dims(attacked_sample[idx], axis=0),
np.expand_dims(attack_target_label[idx], axis=0))
raw_t = time.time() - raw_st
bb_raw_preds.extend(softmax(bb_model.predict(raw_a), axis=1))
raw_query_counts.extend(raw_qc)
raw_query_time.append(raw_t)
for idx in range(benign_size):
raw_st = time.time()
bb_raw_pred = softmax(bb_model.predict(
np.expand_dims(benign_sample[idx], axis=0)),
axis=1)
raw_t = time.time() - raw_st
bb_raw_preds.extend(bb_raw_pred)
raw_query_counts.extend([0])
raw_query_time.append(raw_t)
accuracy_test = np.mean(
np.equal(np.argmax(bb_raw_preds[0:len(attack_target_label)], axis=1),
np.argmax(attack_target_label, axis=1)))
LOGGER.info(TAG, "attack success before adv defense is : %s",
accuracy_test)
# improve the robustness of model with similarity-based detector
bb_def_model = ModelToBeAttacked(bb_net,
defense=True,
train_images=inputs[0:6000])
# attack defensed model
attack_dm = GeneticAttack(model=bb_def_model,
pop_size=6,
mutation_rate=0.05,
per_bounds=0.5,
step_size=0.25,
temp=0.1,
sparse=False)
for idx in range(attacked_size):
def_st = time.time()
_, def_a, def_qc = attack_dm.generate(
np.expand_dims(attacked_sample[idx], axis=0),
np.expand_dims(attack_target_label[idx], axis=0))
def_t = time.time() - def_st
det_res = bb_def_model.get_detected_result()
def_detection_counts.append(np.sum(det_res[-def_qc[0]:]))
bb_def_preds.extend(softmax(bb_def_model.predict(def_a), axis=1))
def_query_counts.extend(def_qc)
def_query_time.append(def_t)
for idx in range(benign_size):
def_st = time.time()
bb_def_pred = softmax(bb_def_model.predict(
np.expand_dims(benign_sample[idx], axis=0)),
axis=1)
def_t = time.time() - def_st
det_res = bb_def_model.get_detected_result()
def_detection_counts.append(np.sum(det_res[-1]))
bb_def_preds.extend(bb_def_pred)
def_query_counts.extend([0])
def_query_time.append(def_t)
accuracy_adv = np.mean(
np.equal(np.argmax(bb_def_preds[0:len(attack_target_label)], axis=1),
np.argmax(attack_target_label, axis=1)))
LOGGER.info(TAG, "attack success rate after adv defense is : %s",
accuracy_adv)
bb_raw_preds = np.array(bb_raw_preds).astype(np.float32)
bb_def_preds = np.array(bb_def_preds).astype(np.float32)
# check evaluate data
max_queries = 6000
def_evaluate = BlackDefenseEvaluate(bb_raw_preds, bb_def_preds,
np.array(raw_query_counts),
np.array(def_query_counts),
np.array(raw_query_time),
np.array(def_query_time),
np.array(def_detection_counts),
true_label, max_queries)
LOGGER.info(
TAG, 'query count variance of adversaries is : {:.2f}'.format(
def_evaluate.qcv()))
LOGGER.info(
TAG, 'attack success rate variance of adversaries '
'is : {:.2f}'.format(def_evaluate.asv()))
LOGGER.info(
TAG, 'false positive rate (FPR) of the query-based detector '
'is : {:.2f}'.format(def_evaluate.fpr()))
LOGGER.info(
TAG, 'the benign query response time variance (QRV) '
'is : {:.2f}'.format(def_evaluate.qrv()))