def inference_network():
# The image is 32 * 32 with RGB representation.
data_shape = [3, 32, 32]
images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32')
#可选的resnet深度20, 32, 44, 56, 110, 1202
'''
实验数据
深度为32
Test with Pass 9, Loss 0.86, Acc 0.76
深度为110
Test with Pass 9, Loss 0.76, Acc 0.76
'''
predict = resnet_cifar10(images, 32)
return predict
def main(use_cuda):
"""
Advbox demo which demonstrate how to use advbox.
"""
TOTAL_NUM = 500
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[3, 32, 32], dtype='float32')
# gradient should flow
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
logits = resnet_cifar10(img, 32)
cost = fluid.layers.cross_entropy(input=logits, label=label)
avg_cost = fluid.layers.mean(x=cost)
#根据配置选择使用CPU资源还是GPU资源
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
BATCH_SIZE = 1
test_reader = paddle.batch(paddle.reader.shuffle(
paddle.dataset.cifar.test10(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
fluid.io.load_params(exe,
"cifar10/resnet/",
main_program=fluid.default_main_program())
# advbox demo
m = PaddleModel(fluid.default_main_program(),
IMG_NAME,
LABEL_NAME,
logits.name,
avg_cost.name, (0, 255),
channel_axis=0)
#形状为[1,28,28] channel_axis=0 形状为[28,28,1] channel_axis=2
attack = SinglePixelAttack(m)
attack_config = {"max_pixels": 32 * 32}
# use test data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in test_reader():
total_count += 1
img = data[0][0]
img = np.reshape(img, [3, 32, 32])
adversary = Adversary(img, data[0][1])
#adversary = Adversary(data[0][0], data[0][1])
# SinglePixelAttack non-targeted attack
adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TEST_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
print("SinglePixelAttack attack done")
def main(use_cuda):
"""
Advbox demo which demonstrate how to use advbox.
"""
TOTAL_NUM = 500
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[3, 32, 32], dtype='float32')
# gradient should flow
img.stop_gradient = False
label = fluid.layers.data(name=LABEL_NAME, shape=[1], dtype='int64')
# logits = mnist_cnn_model(img)
# logits = vgg_bn_drop(img)
logits = resnet_cifar10(img, 32)
cost = fluid.layers.cross_entropy(input=logits, label=label)
avg_cost = fluid.layers.mean(x=cost)
#根据配置选择使用CPU资源还是GPU资源
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
exe = fluid.Executor(place)
BATCH_SIZE = 1
test_reader = paddle.batch(paddle.dataset.cifar.test10(),
batch_size=BATCH_SIZE)
fluid.io.load_params(exe,
"cifar10/resnet",
main_program=fluid.default_main_program())
# advbox demo
m = PaddleModel(fluid.default_main_program(),
IMG_NAME,
LABEL_NAME,
logits.name,
avg_cost.name, (-1, 1),
channel_axis=1)
# attack = FGSM(m)
attack = DeepFoolAttack(m)
# attack = FGSMT(m)
# attack_config = {"epsilons": 0.3}
attack_config = {"iterations": 100, "overshoot": 9}
# use test data to generate adversarial examples
total_count = 0
fooling_count = 0
for data in test_reader():
total_count += 1
adversary = Adversary(data[0][0], data[0][1])
# FGSM non-targeted attack
adversary = attack(adversary, **attack_config)
# FGSMT targeted attack
# tlabel = 0
# adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# adversary = attack(adversary, **attack_config)
if adversary.is_successful():
fooling_count += 1
print(
'attack success, original_label=%d, adversarial_label=%d, count=%d'
% (data[0][1], adversary.adversarial_label, total_count))
# plt.imshow(adversary.target, cmap='Greys_r')
# plt.show()
# np.save('adv_img', adversary.target)
else:
print('attack failed, original_label=%d, count=%d' %
(data[0][1], total_count))
if total_count >= TOTAL_NUM:
print(
"[TEST_DATASET]: fooling_count=%d, total_count=%d, fooling_rate=%f"
% (fooling_count, total_count,
float(fooling_count) / total_count))
break
# print("fgsm attack done")
print("deelfool attack done")