def main(image_path):
alexnet = mx.gluon.model_zoo.vision.alexnet(pretrained=True)
# print(alexnet)
orig = cv2.imread(image_path)[..., ::-1]
orig = cv2.resize(orig, (224, 224))
img = orig.copy().astype(np.float32)
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
img /= 255.0
img = old_div((img - mean), std)
img = img.transpose(2, 0, 1)
img = np.expand_dims(img, axis=0)
#array = mx.nd.array(img)
# advbox demo
m = MxNetModel(alexnet, None, (-1, 1), channel_axis=1)
attack = FGSMT(m)
#attack = FGSM(m)
# 静态epsilons
attack_config = {"epsilons": 0.2, "epsilon_steps": 1, "steps": 100}
inputs = img
#labels=388
labels = None
print(inputs.shape)
adversary = Adversary(inputs, labels)
#adversary = Adversary(inputs, 388)
tlabel = 538
adversary.set_target(is_targeted_attack=True, target_label=tlabel)
adversary = attack(adversary, **attack_config)
if adversary.is_successful():
print('attack success, adversarial_label=%d' %
(adversary.adversarial_label))
adv = adversary.adversarial_example[0]
adv = adv.transpose(1, 2, 0)
adv = (adv * std) + mean
adv = adv * 255.0
adv = adv[..., ::-1] # RGB to BGR
adv = np.clip(adv, 0, 255).astype(np.uint8)
cv2.imwrite('img_adv.png', adv)
else:
print('attack failed')
print("fgsm attack done")
def main():
"""
Advbox demo which demonstrate how to use advbox.
"""
TOTAL_NUM = 50
IMG_NAME = 'img'
LABEL_NAME = 'label'
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], 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)
# use CPU
place = fluid.CPUPlace()
# use GPU
#place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
BATCH_SIZE = 1
test_reader = paddle.batch(paddle.reader.shuffle(
paddle.dataset.mnist.test(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
fluid.io.load_params(exe,
"mnist/",
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 = FGSMT(m)
attack_config = {"epsilons": 0.3}
# 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 = 8
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))
adversarial_example = adversary.adversarial_example
#print adversarial_example
#原始数据归一化到(-1,1)之间了 需要还原到(0,255)
adversarial_example /= 2.
adversarial_example += 0.5
adversarial_example *= 255.
adversarial_example = adversarial_example.astype(np.uint8)
#print adversarial_example
adversarial_example = np.reshape(adversarial_example, (28, 28))
im = Image.fromarray(adversarial_example)
filename = "original-%d-adversarial-%d-targeted-by-fgsm.jpg" % (
data[0][1], adversary.adversarial_label)
im.save("output/" + filename)
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("fgsmt attack done")
def main(modulename, imagename):
'''
Kera的应用模块Application提供了带有预训练权重的Keras模型，这些模型可以用来进行预测、特征提取和finetune
模型的预训练权重将下载到~/.keras/models/并在载入模型时自动载入
'''
# 设置为测试模式
keras.backend.set_learning_phase(0)
model = ResNet50(weights=modulename)
#model = InceptionV3(weights=modulename)
logging.info(model.summary())
img = image.load_img(imagename, target_size=(224, 224))
raw_imagedata = image.img_to_array(img)
raw_imagedata = np.expand_dims(raw_imagedata, axis=0)
# 'RGB'->'BGR'
imagedata = raw_imagedata[:, :, :, ::-1]
#logging.info(raw_imagedata)
#logging.info(imagedata)
#logit fc1000
logits = model.get_layer('fc1000').output
#keras中获取指定层的方法为：
#base_model.get_layer('block4_pool').output)
# advbox demo
# 因为原始数据没有归一化 所以bounds=(0, 255) KerasMode内部在进行预测和计算梯度时会进行预处理
# imagenet数据集归一化时 标准差为1 mean为[104, 116, 123]
# featurefqueezing_bit_depth featurefqueezing防御算法 提高生成攻击样本的质量 为特征数据的bit位 一般8就ok了
m = KerasModel(model,
model.input,
None,
logits,
None,
bounds=(0, 255.0),
channel_axis=3,
preprocess=([104, 116, 123], 1),
featurefqueezing_bit_depth=8)
attack = FGSM(m)
#设置epsilons时不用考虑特征范围 算法实现时已经考虑了取值范围的问题 epsilons取值范围为（0，1）
#epsilon支持动态调整 epsilon_steps为epsilon变化的个数
#epsilons为下限 epsilons_max为上限
#attack_config = {"epsilons": 0.3, "epsilons_max": 0.5, "epsilon_steps": 100}
#静态epsilons
attack_config = {
"epsilons": 1,
"epsilons_max": 10,
"epsilon_steps": 1,
"steps": 100
}
#y设置为空 会自动计算
adversary = Adversary(imagedata.copy(), None)
# FGSM non-targeted attack
adversary = attack(adversary, **attack_config)
if adversary.is_successful():
print('attack success, adversarial_label=%d' %
(adversary.adversarial_label))
#对抗样本保存在adversary.adversarial_example
adversary_image = np.copy(adversary.adversarial_example)
logging.info("adversary_image label={0} ".format(
np.argmax(m.predict(adversary_image))))
#logging.info(adversary_image)
#强制类型转换 之前是float 现在要转换成uint8
adversary_image = np.array(adversary_image).astype("uint8").reshape(
[224, 224, 3])
#logging.info(adversary_image)
adversary_image = adversary_image[:, :, ::-1]
logging.info(adversary_image - raw_imagedata)
img = array_to_img(adversary_image)
img.save('adversary_image_nontarget.jpg')
print("fgsm non-target attack done")
attack = FGSMT(m)
#静态epsilons
attack_config = {
"epsilons": 10,
"epsilons_max": 10,
"epsilon_steps": 1,
"steps": 100
}
adversary = Adversary(imagedata, None)
tlabel = 489
adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# FGSM targeted attack
adversary = attack(adversary, **attack_config)
if adversary.is_successful():
print('attack success, adversarial_label=%d' %
(adversary.adversarial_label))
#对抗样本保存在adversary.adversarial_example
adversary_image = np.copy(adversary.adversarial_example)
#强制类型转换 之前是float 现在要转换成int8
adversary_image = np.array(adversary_image).astype("uint8").reshape(
[224, 224, 3])
adversary_image = adversary_image[:, :, ::-1]
logging.info(adversary_image - raw_imagedata)
img = array_to_img(adversary_image)
img.save('adversary_image_target.jpg')
print("fgsm target attack done")
def get_adversarial_examples_from_model2():
"""
Advbox demo which demonstrate how to use advbox.
"""
TOTAL_NUM = 500
IMG_NAME = 'img'
LABEL_NAME = 'label'
#保存对抗样本
x = []
y = []
img = fluid.layers.data(name=IMG_NAME, shape=[1, 28, 28], 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 = mnist_mlp_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)
# use CPU
place = fluid.CPUPlace()
# use GPU
#place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
BATCH_SIZE = 1
test_reader = paddle.batch(paddle.reader.shuffle(
paddle.dataset.mnist.test(), buf_size=128 * 10),
batch_size=BATCH_SIZE)
#fluid.io.load_params(
# exe, model1_path, main_program=fluid.default_main_program())
fluid.io.load_params(exe,
model2_path,
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 = FGSMT(m)
attack_config = {"epsilons": 0.3}
# 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 = 8
#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))
adversarial_example = adversary.adversarial_example
x.append(adversarial_example)
y.append(data[0][1])
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")
return x, y
def main(image_path):
# Define what device we are using
logging.info("CUDA Available: {}".format(torch.cuda.is_available()))
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
orig = cv2.imread(image_path)[..., ::-1]
orig = cv2.resize(orig, (224, 224))
img = orig.copy().astype(np.float32)
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
img /= 255.0
img = (img - mean) / std
img = img.transpose(2, 0, 1)
img = Variable(
torch.from_numpy(img).to(device).float().unsqueeze(0)).cpu().numpy()
# Initialize the network
#Alexnet
model = models.alexnet(pretrained=True).to(device).eval()
#model = models.resnet18(pretrained=True).to(device).eval()
#print(model)
#设置为不保存梯度值 自然也无法修改
for param in model.parameters():
#print(param)
#print(param.requires_grad)
param.requires_grad = False
#loss_func=nn.CrossEntropyLoss()
# advbox demo
m = PytorchModel(model, None, (-1, 1), channel_axis=1)
attack = FGSMT(m)
#attack = FGSM(m)
# 静态epsilons
attack_config = {"epsilons": 0.2, "epsilon_steps": 1, "steps": 100}
inputs = img
#labels=388
labels = None
print(inputs.shape)
adversary = Adversary(inputs, labels)
#adversary = Adversary(inputs, 388)
tlabel = 538
adversary.set_target(is_targeted_attack=True, target_label=tlabel)
adversary = attack(adversary, **attack_config)
if adversary.is_successful():
print('attack success, adversarial_label=%d' %
(adversary.adversarial_label))
adv = adversary.adversarial_example[0]
adv = adv.transpose(1, 2, 0)
adv = (adv * std) + mean
adv = adv * 255.0
adv = adv[..., ::-1] # RGB to BGR
adv = np.clip(adv, 0, 255).astype(np.uint8)
cv2.imwrite('img_adv.png', adv)
else:
print('attack failed')
print("fgsm attack done")
def main(dirname, imagename):
#加载解码的图像 这里是个大坑 tf提供的imagenet预训练好的模型pb文件中 包含针对图像的预处理环节 即解码jpg文件 这部分没有梯度
#需要直接处理解码后的数据
image = None
with tf.gfile.Open(imagename, 'rb') as f:
image = np.array(Image.open(f).convert('RGB')).astype(np.float)
image = [image]
session = tf.Session()
def create_graph(dirname):
with tf.gfile.FastGFile(dirname, 'rb') as f:
graph_def = session.graph_def
graph_def.ParseFromString(f.read())
_ = tf.import_graph_def(graph_def, name='')
create_graph(dirname)
# 初始化参数 非常重要
session.run(tf.global_variables_initializer())
tensorlist = [n.name for n in session.graph_def.node]
logger.info(tensorlist)
#获取logits
logits = session.graph.get_tensor_by_name('softmax/logits:0')
x = session.graph.get_tensor_by_name('ExpandDims:0')
#y = tf.placeholder(tf.int64, None, name='label')
# advbox demo
# 因为原始数据没有归一化 所以bounds=(0, 255)
m = TensorflowModel(session,
x,
None,
logits,
None,
bounds=(0, 255),
channel_axis=3,
preprocess=None)
attack = FGSM(m)
#设置epsilons时不用考虑特征范围 算法实现时已经考虑了取值范围的问题 epsilons取值范围为（0，1）
#epsilon支持动态调整 epsilon_steps为epsilon变化的个数
#epsilons为下限 epsilons_max为上限
attack_config = {
"epsilons": 20,
"epsilons_max": 10,
"epsilon_steps": 1,
"steps": 100
}
#y设置为空 会自动计算
adversary = Adversary(image, None)
# FGSM non-targeted attack
adversary = attack(adversary, **attack_config)
if adversary.is_successful():
print('attack success, adversarial_label=%d' %
(adversary.adversarial_label))
#对抗样本保存在adversary.adversarial_example
adversary_image = np.copy(adversary.adversarial_example)
#强制类型转换 之前是float 现在要转换成int8
#print(adversary_image)
adversary_image = np.array(adversary_image).astype("uint8").reshape(
[100, 100, 3])
logging.info(adversary_image - image)
im = Image.fromarray(adversary_image)
im.save("adversary_image_nontarget.jpg")
print("fgsm non-target attack done")
attack = FGSMT(m)
attack_config = {
"epsilons": 30,
"epsilons_max": 10,
"epsilon_steps": 1,
"steps": 100
}
adversary = Adversary(image, None)
#麦克风
tlabel = 651
adversary.set_target(is_targeted_attack=True, target_label=tlabel)
# FGSM targeted attack
adversary = attack(adversary, **attack_config)
if adversary.is_successful():
print('attack success, adversarial_label=%d' %
(adversary.adversarial_label))
#对抗样本保存在adversary.adversarial_example
adversary_image = np.copy(adversary.adversarial_example)
#强制类型转换 之前是float 现在要转换成int8
adversary_image = np.array(adversary_image).astype("uint8").reshape(
[100, 100, 3])
logging.info(adversary_image - image)
im = Image.fromarray(adversary_image)
im.save("adversary_image_target.jpg")
print("fgsm target attack done")
def main():
"""
Advbox demo which demonstrate how to use advbox.
"""
TOTAL_NUM = 50
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)
# use CPU
place = fluid.CPUPlace()
# use GPU
#place = fluid.CUDAPlace(0)
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/", 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 = FGSMT(m)
attack_config = {"epsilons": 0.3}
# 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("fgsmt attack done")