def test_fuzzing_ascend():
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
# load network
net = Net()
model = Model(net)
batch_size = 8
num_classe = 10
mutate_config = [{
'method': 'Blur',
'params': {
'auto_param': [True]
}
}, {
'method': 'Contrast',
'params': {
'factor': [2, 1]
}
}, {
'method': 'Translate',
'params': {
'x_bias': [0.1, 0.3],
'y_bias': [0.2]
}
}, {
'method': 'FGSM',
'params': {
'eps': [0.1, 0.2, 0.3],
'alpha': [0.1]
}
}]
# initialize fuzz test with training dataset
neuron_num = 10
segmented_num = 1000
train_images = np.random.rand(32, 1, 32, 32).astype(np.float32)
model_coverage_test = ModelCoverageMetrics(model, neuron_num,
segmented_num, train_images)
# fuzz test with original test data
# get test data
test_images = np.random.rand(batch_size, 1, 32, 32).astype(np.float32)
test_labels = np.random.randint(num_classe,
size=batch_size).astype(np.int32)
test_labels = (np.eye(num_classe)[test_labels]).astype(np.float32)
initial_seeds = []
# make initial seeds
for img, label in zip(test_images, test_labels):
initial_seeds.append([img, label])
initial_seeds = initial_seeds[:100]
model_coverage_test.calculate_coverage(
np.array(test_images[:100]).astype(np.float32))
LOGGER.info(TAG, 'KMNC of this test is : %s',
model_coverage_test.get_kmnc())
model_fuzz_test = Fuzzer(model, train_images, neuron_num, segmented_num)
_, _, _, _, metrics = model_fuzz_test.fuzzing(mutate_config, initial_seeds)
print(metrics)
def test_lenet_mnist_coverage_cpu():
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
# load network
net = Net()
model = Model(net)
# initialize fuzz test with training dataset
neuron_num = 10
segmented_num = 1000
training_data = (np.random.random((10000, 10)) * 20).astype(np.float32)
model_fuzz_test = ModelCoverageMetrics(model, neuron_num, segmented_num,
training_data)
# fuzz test with original test data
# get test data
test_data = (np.random.random((2000, 10)) * 20).astype(np.float32)
test_labels = np.random.randint(0, 10, 2000).astype(np.int32)
model_fuzz_test.calculate_coverage(test_data)
LOGGER.info(TAG, 'KMNC of this test is : %s', model_fuzz_test.get_kmnc())
LOGGER.info(TAG, 'NBC of this test is : %s', model_fuzz_test.get_nbc())
LOGGER.info(TAG, 'SNAC of this test is : %s', model_fuzz_test.get_snac())
# generate adv_data
loss = SoftmaxCrossEntropyWithLogits(sparse=True)
attack = FastGradientSignMethod(net, eps=0.3, loss_fn=loss)
adv_data = attack.batch_generate(test_data, test_labels, batch_size=32)
model_fuzz_test.calculate_coverage(adv_data, bias_coefficient=0.5)
LOGGER.info(TAG, 'KMNC of this test is : %s', model_fuzz_test.get_kmnc())
LOGGER.info(TAG, 'NBC of this test is : %s', model_fuzz_test.get_nbc())
LOGGER.info(TAG, 'SNAC of this test is : %s', model_fuzz_test.get_snac())
def test_lenet_mnist_coverage():
# upload trained network
ckpt_path = '../common/networks/lenet5/trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
net = LeNet5()
load_dict = load_checkpoint(ckpt_path)
load_param_into_net(net, load_dict)
model = Model(net)
# get training data
data_list = "../common/dataset/MNIST/train"
batch_size = 32
ds = generate_mnist_dataset(data_list, batch_size, sparse=True)
train_images = []
for data in ds.create_tuple_iterator(output_numpy=True):
images = data[0].astype(np.float32)
train_images.append(images)
train_images = np.concatenate(train_images, axis=0)
# initialize fuzz test with training dataset
model_fuzz_test = ModelCoverageMetrics(model, 10, 1000, train_images)
# fuzz test with original test data
# get test data
data_list = "../common/dataset/MNIST/test"
batch_size = 32
ds = generate_mnist_dataset(data_list, batch_size, sparse=True)
test_images = []
test_labels = []
for data in ds.create_tuple_iterator(output_numpy=True):
images = data[0].astype(np.float32)
labels = data[1]
test_images.append(images)
test_labels.append(labels)
test_images = np.concatenate(test_images, axis=0)
test_labels = np.concatenate(test_labels, axis=0)
model_fuzz_test.calculate_coverage(test_images)
LOGGER.info(TAG, 'KMNC of this test is : %s', model_fuzz_test.get_kmnc())
LOGGER.info(TAG, 'NBC of this test is : %s', model_fuzz_test.get_nbc())
LOGGER.info(TAG, 'SNAC of this test is : %s', model_fuzz_test.get_snac())
# generate adv_data
loss = SoftmaxCrossEntropyWithLogits(sparse=True)
attack = FastGradientSignMethod(net, eps=0.3, loss_fn=loss)
adv_data = attack.batch_generate(test_images, test_labels, batch_size=32)
model_fuzz_test.calculate_coverage(adv_data, bias_coefficient=0.5)
LOGGER.info(TAG, 'KMNC of this adv data is : %s', model_fuzz_test.get_kmnc())
LOGGER.info(TAG, 'NBC of this adv data is : %s', model_fuzz_test.get_nbc())
LOGGER.info(TAG, 'SNAC of this adv data is : %s', model_fuzz_test.get_snac())
def test_lenet_mnist_coverage_ascend():
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
# load network
net = Net()
model = Model(net)
# initialize fuzz test with training dataset
training_data = (np.random.random((10000, 10))*20).astype(np.float32)
model_fuzz_test = ModelCoverageMetrics(model, 10, 1000, training_data)
# fuzz test with original test data
# get test data
test_data = (np.random.random((2000, 10))*20).astype(np.float32)
test_labels = np.random.randint(0, 10, 2000)
test_labels = (np.eye(10)[test_labels]).astype(np.float32)
model_fuzz_test.calculate_coverage(test_data)
LOGGER.info(TAG, 'KMNC of this test is : %s', model_fuzz_test.get_kmnc())
LOGGER.info(TAG, 'NBC of this test is : %s', model_fuzz_test.get_nbc())
LOGGER.info(TAG, 'SNAC of this test is : %s', model_fuzz_test.get_snac())
# generate adv_data
attack = FastGradientSignMethod(net, eps=0.3)
adv_data = attack.batch_generate(test_data, test_labels, batch_size=32)
model_fuzz_test.calculate_coverage(adv_data, bias_coefficient=0.5)
LOGGER.info(TAG, 'KMNC of this test is : %s', model_fuzz_test.get_kmnc())
LOGGER.info(TAG, 'NBC of this test is : %s', model_fuzz_test.get_nbc())
LOGGER.info(TAG, 'SNAC of this test is : %s', model_fuzz_test.get_snac())
def test_lenet_mnist_fuzzing():
# upload trained network
ckpt_path = '../common/networks/lenet5/trained_ckpt_file/checkpoint_lenet-10_1875.ckpt'
net = LeNet5()
load_dict = load_checkpoint(ckpt_path)
load_param_into_net(net, load_dict)
model = Model(net)
mutate_config = [{'method': 'Blur',
'params': {'radius': [0.1, 0.2, 0.3],
'auto_param': [True, False]}},
{'method': 'Contrast',
'params': {'auto_param': [True]}},
{'method': 'Translate',
'params': {'auto_param': [True]}},
{'method': 'Brightness',
'params': {'auto_param': [True]}},
{'method': 'Noise',
'params': {'auto_param': [True]}},
{'method': 'Scale',
'params': {'auto_param': [True]}},
{'method': 'Shear',
'params': {'auto_param': [True]}},
{'method': 'FGSM',
'params': {'eps': [0.3, 0.2, 0.4], 'alpha': [0.1]}}
]
# get training data
data_list = "../common/dataset/MNIST/train"
batch_size = 32
ds = generate_mnist_dataset(data_list, batch_size, sparse=False)
train_images = []
for data in ds.create_tuple_iterator(output_numpy=True):
images = data[0].astype(np.float32)
train_images.append(images)
train_images = np.concatenate(train_images, axis=0)
neuron_num = 10
segmented_num = 1000
# initialize fuzz test with training dataset
model_coverage_test = ModelCoverageMetrics(model, neuron_num, segmented_num, train_images)
# fuzz test with original test data
# get test data
data_list = "../common/dataset/MNIST/test"
batch_size = 32
ds = generate_mnist_dataset(data_list, batch_size, sparse=False)
test_images = []
test_labels = []
for data in ds.create_tuple_iterator(output_numpy=True):
images = data[0].astype(np.float32)
labels = data[1]
test_images.append(images)
test_labels.append(labels)
test_images = np.concatenate(test_images, axis=0)
test_labels = np.concatenate(test_labels, axis=0)
initial_seeds = []
# make initial seeds
for img, label in zip(test_images, test_labels):
initial_seeds.append([img, label])
initial_seeds = initial_seeds[:100]
model_coverage_test.calculate_coverage(
np.array(test_images[:100]).astype(np.float32))
LOGGER.info(TAG, 'KMNC of this test is : %s',
model_coverage_test.get_kmnc())
model_fuzz_test = Fuzzer(model, train_images, neuron_num, segmented_num)
_, _, _, _, metrics = model_fuzz_test.fuzzing(mutate_config, initial_seeds, eval_metrics='auto')
if metrics:
for key in metrics:
LOGGER.info(TAG, key + ': %s', metrics[key])
def example_lenet_mnist_fuzzing():
"""
An example of fuzz testing and then enhance the non-robustness model.
"""
# upload trained network
ckpt_path = '../common/networks/lenet5/trained_ckpt_file/lenet_m1-10_1250.ckpt'
net = LeNet5()
load_dict = load_checkpoint(ckpt_path)
load_param_into_net(net, load_dict)
model = Model(net)
mutate_config = [{'method': 'Blur',
'params': {'auto_param': [True]}},
{'method': 'Contrast',
'params': {'auto_param': [True]}},
{'method': 'Translate',
'params': {'auto_param': [True]}},
{'method': 'Brightness',
'params': {'auto_param': [True]}},
{'method': 'Noise',
'params': {'auto_param': [True]}},
{'method': 'Scale',
'params': {'auto_param': [True]}},
{'method': 'Shear',
'params': {'auto_param': [True]}},
{'method': 'FGSM',
'params': {'eps': [0.3, 0.2, 0.4], 'alpha': [0.1]}}
]
# get training data
data_list = "../common/dataset/MNIST/train"
batch_size = 32
ds = generate_mnist_dataset(data_list, batch_size, sparse=False)
train_images = []
for data in ds.create_tuple_iterator(output_numpy=True):
images = data[0].astype(np.float32)
train_images.append(images)
train_images = np.concatenate(train_images, axis=0)
# initialize fuzz test with training dataset
model_coverage_test = ModelCoverageMetrics(model, 10, 1000, train_images)
# fuzz test with original test data
# get test data
data_list = "../common/dataset/MNIST/test"
batch_size = 32
init_samples = 5000
max_iters = 50000
mutate_num_per_seed = 10
ds = generate_mnist_dataset(data_list, batch_size, num_samples=init_samples,
sparse=False)
test_images = []
test_labels = []
for data in ds.create_tuple_iterator(output_numpy=True):
images = data[0].astype(np.float32)
labels = data[1]
test_images.append(images)
test_labels.append(labels)
test_images = np.concatenate(test_images, axis=0)
test_labels = np.concatenate(test_labels, axis=0)
initial_seeds = []
# make initial seeds
for img, label in zip(test_images, test_labels):
initial_seeds.append([img, label])
model_coverage_test.calculate_coverage(
np.array(test_images[:100]).astype(np.float32))
LOGGER.info(TAG, 'KMNC of test dataset before fuzzing is : %s',
model_coverage_test.get_kmnc())
LOGGER.info(TAG, 'NBC of test dataset before fuzzing is : %s',
model_coverage_test.get_nbc())
LOGGER.info(TAG, 'SNAC of test dataset before fuzzing is : %s',
model_coverage_test.get_snac())
model_fuzz_test = Fuzzer(model, train_images, 10, 1000)
gen_samples, gt, _, _, metrics = model_fuzz_test.fuzzing(mutate_config,
initial_seeds,
eval_metrics='auto',
max_iters=max_iters,
mutate_num_per_seed=mutate_num_per_seed)
if metrics:
for key in metrics:
LOGGER.info(TAG, key + ': %s', metrics[key])
def split_dataset(image, label, proportion):
"""
Split the generated fuzz data into train and test set.
"""
indices = np.arange(len(image))
random.shuffle(indices)
train_length = int(len(image) * proportion)
train_image = [image[i] for i in indices[:train_length]]
train_label = [label[i] for i in indices[:train_length]]
test_image = [image[i] for i in indices[:train_length]]
test_label = [label[i] for i in indices[:train_length]]
return train_image, train_label, test_image, test_label
train_image, train_label, test_image, test_label = split_dataset(
gen_samples, gt, 0.7)
# load model B and test it on the test set
ckpt_path = '../common/networks/lenet5/trained_ckpt_file/lenet_m2-10_1250.ckpt'
net = LeNet5()
load_dict = load_checkpoint(ckpt_path)
load_param_into_net(net, load_dict)
model_b = Model(net)
pred_b = model_b.predict(Tensor(test_image, dtype=mindspore.float32)).asnumpy()
acc_b = np.sum(np.argmax(pred_b, axis=1) == np.argmax(test_label, axis=1)) / len(test_label)
print('Accuracy of model B on test set is ', acc_b)
# enhense model robustness
lr = 0.001
momentum = 0.9
loss_fn = SoftmaxCrossEntropyWithLogits(Sparse=True)
optimizer = Momentum(net.trainable_params(), lr, momentum)
adv_defense = AdversarialDefense(net, loss_fn, optimizer)
adv_defense.batch_defense(np.array(train_image).astype(np.float32),
np.argmax(train_label, axis=1).astype(np.int32))
preds_en = net(Tensor(test_image, dtype=mindspore.float32)).asnumpy()
acc_en = np.sum(np.argmax(preds_en, axis=1) == np.argmax(test_label, axis=1)) / len(test_label)
print('Accuracy of enhensed model on test set is ', acc_en)