对抗示例生成
Ascend
GPU
CPU
进阶
计算机视觉
全流程
近年来随着数据、计算能力、理论的不断发展演进,深度学习在图像、文本、语音、自动驾驶等众多领域都得到了广泛应用。与此同时,人们也越来越关注各类模型在使用过程中的安全问题,因为AI模型很容易受到外界有意无意的攻击而产生错误的结果。在本案例中,我们将以梯度符号攻击FGSM(Fast Gradient Sign Method)为例,演示此类攻击是如何误导模型的。
本篇基于CPU/GPU/Ascend环境运行。
对抗样本定义
Szegedy在2013年最早提出对抗样本的概念:在原始样本处加入人类无法察觉的微小扰动,使得深度模型性能下降,这种样本即对抗样本。如下图所示,本来预测为“panda”的图像在添加噪声之后,模型就将其预测为“gibbon”,右边的样本就是一个对抗样本:
攻击方法
对模型的攻击方法可以按照以下方法分类:
攻击者掌握的信息多少:
白盒攻击:攻击者具有对模型的全部知识和访问权限,包括模型结构、权重、输入、输出。攻击者在产生对抗性攻击数据的过程中能够与模型系统有所交互。攻击者可以针对被攻击模型的特性设计特定的攻击算法。
黑盒攻击:与白盒攻击相反,攻击者仅具有关于模型的有限知识。攻击者对模型的结构权重一无所知,仅了解部分输入输出。
攻击者的目的:
有目标的攻击:攻击者将模型结果误导为特定分类。
无目标的攻击:攻击者只想产生错误结果,而不在乎新结果是什么。
本案例中用到的FGSM是一种白盒攻击方法,既可以是有目标也可以是无目标攻击。
更多的模型安全功能可参考MindArmour,现支持FGSM、LLC、Substitute Attack等多种对抗样本生成方法,并提供对抗样本鲁棒性模块、Fuzz Testing模块、隐私保护与评估模块,帮助用户增强模型安全性。
快速梯度符号攻击(FGSM)
正常分类网络的训练会定义一个损失函数,用于衡量模型输出值与样本真实标签的距离,通过反向传播计算模型梯度,梯度下降更新网络参数,减小损失值,提升模型精度。
FGSM(Fast Gradient Sign Method)是一种简单高效的对抗样本生成方法。不同于正常分类网络的训练过程,FGSM通过计算loss对于输入的梯度\(\nabla_x J(\theta ,x ,y)\),这个梯度表征了loss对于输入变化的敏感性。然后在原始输入加上上述梯度,使得loss增大,模型对于改造后的输入样本分类效果变差,达到攻击效果。对抗样本的另一要求是生成样本与原始样本的差异要尽可能的小,使用sign函数可以使得修改图片时尽可能的均匀。
产生的对抗扰动用公式可以表示为:
对抗样本可公式化为:
其中,
\(x\):正确分类为“Pandas”的原始输入图像。
\(y\):是\(x\)的输出。
\(\theta\):模型参数。
\(\varepsilon\):攻击系数。
\(J(\theta, x, y)\):训练网络的损失。
\(\nabla_x J(\theta)\):反向传播梯度。
实验前准备
导入模型训练需要的库
本案例将使用MNIST训练一个精度达标的LeNet网络,然后运行上文中所提到的FGSM攻击方法,实现错误分类的效果。
首先导入模型训练需要的库
[1]:
import os
import numpy as np
from mindspore import Tensor, context, Model, load_checkpoint, load_param_into_net
import mindspore.nn as nn
import mindspore.ops as ops
from mindspore.common.initializer import Normal
from mindspore.train.callback import LossMonitor, ModelCheckpoint, CheckpointConfig
import mindspore.dataset as ds
import mindspore.dataset.transforms.c_transforms as C
import mindspore.dataset.vision.c_transforms as CV
from mindspore.dataset.vision import Inter
from mindspore import dtype as mstype
下载数据集
以下示例代码将数据集下载并解压到指定位置。
[ ]:
import os
import requests
requests.packages.urllib3.disable_warnings()
def download_dataset(dataset_url, path):
filename = dataset_url.split("/")[-1]
save_path = os.path.join(path, filename)
if os.path.exists(save_path):
return
if not os.path.exists(path):
os.makedirs(path)
res = requests.get(dataset_url, stream=True, verify=False)
with open(save_path, "wb") as f:
for chunk in res.iter_content(chunk_size=512):
if chunk:
f.write(chunk)
print("The {} file is downloaded and saved in the path {} after processing".format(os.path.basename(dataset_url), path))
train_path = "datasets/MNIST_Data/train"
test_path = "datasets/MNIST_Data/test"
download_dataset("https://mindspore-website.obs.myhuaweicloud.com/notebook/datasets/mnist/train-labels-idx1-ubyte", train_path)
download_dataset("https://mindspore-website.obs.myhuaweicloud.com/notebook/datasets/mnist/train-images-idx3-ubyte", train_path)
download_dataset("https://mindspore-website.obs.myhuaweicloud.com/notebook/datasets/mnist/t10k-labels-idx1-ubyte", test_path)
download_dataset("https://mindspore-website.obs.myhuaweicloud.com/notebook/datasets/mnist/t10k-images-idx3-ubyte", test_path)
下载的数据集文件的目录结构如下:
./datasets/MNIST_Data
├── test
│ ├── t10k-images-idx3-ubyte
│ └── t10k-labels-idx1-ubyte
└── train
├── train-images-idx3-ubyte
└── train-labels-idx1-ubyte
攻击准备
在完成准备工作之后,开始训练精度达标的LeNet网络。
采用GRAPH_MODE
在CPU/GPU/Ascend中运行本案例,下面将硬件设定为Ascend:
[3]:
context.set_context(mode=context.GRAPH_MODE, device_target='Ascend')
训练LeNet网络
实验中使用LeNet作为演示模型完成图像分类,这里先定义网络并使用MNIST数据集进行训练。
定义LeNet网络:
[4]:
class LeNet5(nn.Cell):
def __init__(self, num_class=10, num_channel=1):
super(LeNet5, self).__init__()
self.conv1 = nn.Conv2d(num_channel, 6, 5, pad_mode='valid')
self.conv2 = nn.Conv2d(6, 16, 5, pad_mode='valid')
self.fc1 = nn.Dense(16 * 5 * 5, 120, weight_init=Normal(0.02))
self.fc2 = nn.Dense(120, 84, weight_init=Normal(0.02))
self.fc3 = nn.Dense(84, num_class, weight_init=Normal(0.02))
self.relu = nn.ReLU()
self.max_pool2d = nn.MaxPool2d(kernel_size=2, stride=2)
self.flatten = nn.Flatten()
def construct(self, x):
x = self.conv1(x)
x = self.relu(x)
x = self.max_pool2d(x)
x = self.conv2(x)
x = self.relu(x)
x = self.max_pool2d(x)
x = self.flatten(x)
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
x = self.relu(x)
x = self.fc3(x)
return x
net = LeNet5()
进行数据处理:
[5]:
def create_dataset(data_path, batch_size=1, repeat_size=1, num_parallel_workers=1):
# 定义数据集
mnist_ds = ds.MnistDataset(data_path)
resize_height, resize_width = 32, 32
rescale = 1.0 / 255.0
shift = 0.0
rescale_nml = 1 / 0.3081
shift_nml = -1 * 0.1307 / 0.3081
# 定义所需要操作的map映射
resize_op = CV.Resize((resize_height, resize_width), interpolation=Inter.LINEAR)
rescale_nml_op = CV.Rescale(rescale_nml, shift_nml)
rescale_op = CV.Rescale(rescale, shift)
hwc2chw_op = CV.HWC2CHW()
type_cast_op = C.TypeCast(mstype.int32)
# 使用map映射函数,将数据操作应用到数据集
mnist_ds = mnist_ds.map(operations=type_cast_op, input_columns="label", num_parallel_workers=num_parallel_workers)
mnist_ds = mnist_ds.map(operations=resize_op, input_columns="image", num_parallel_workers=num_parallel_workers)
mnist_ds = mnist_ds.map(operations=rescale_op, input_columns="image", num_parallel_workers=num_parallel_workers)
mnist_ds = mnist_ds.map(operations=rescale_nml_op, input_columns="image", num_parallel_workers=num_parallel_workers)
mnist_ds = mnist_ds.map(operations=hwc2chw_op, input_columns="image", num_parallel_workers=num_parallel_workers)
# 进行shuffle、batch、repeat操作
buffer_size = 10000
mnist_ds = mnist_ds.shuffle(buffer_size=buffer_size)
mnist_ds = mnist_ds.batch(batch_size, drop_remainder=True)
mnist_ds = mnist_ds.repeat(count=repeat_size)
return mnist_ds
定义优化器与损失函数:
[6]:
net_loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
net_opt = nn.Momentum(net.trainable_params(), learning_rate=0.01, momentum=0.9)
定义网络参数:
[7]:
config_ck = CheckpointConfig(save_checkpoint_steps=1875, keep_checkpoint_max=10)
ckpoint = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck)
定义LeNet网络的训练函数和测试函数:
[8]:
def test_net(model, data_path):
ds_eval = create_dataset(os.path.join(data_path, "test"))
acc = model.eval(ds_eval, dataset_sink_mode=False)
print("{}".format(acc))
def train_net(model, epoch_size, data_path, repeat_size, ckpoint_cb, sink_mode):
ds_train = create_dataset(os.path.join(data_path, "train"), 32, repeat_size)
model.train(epoch_size, ds_train, callbacks=[ckpoint_cb, LossMonitor(125)], dataset_sink_mode=sink_mode)
[9]:
train_epoch = 1
mnist_path = "./datasets/MNIST_Data/"
repeat_size = 1
model = Model(net, net_loss, net_opt, metrics={"Accuracy": nn.Accuracy()})
训练LeNet网络:
[10]:
train_net(model, train_epoch, mnist_path, repeat_size, ckpoint, False)
epoch: 1 step: 125, loss is 2.3018382
epoch: 1 step: 250, loss is 2.2910337
epoch: 1 step: 375, loss is 2.2876282
epoch: 1 step: 500, loss is 2.293197
epoch: 1 step: 625, loss is 2.2983356
epoch: 1 step: 750, loss is 0.73134214
epoch: 1 step: 875, loss is 0.39000687
epoch: 1 step: 1000, loss is 0.12004304
epoch: 1 step: 1125, loss is 0.10009943
epoch: 1 step: 1250, loss is 0.31425583
epoch: 1 step: 1375, loss is 0.14330618
epoch: 1 step: 1500, loss is 0.05759584
epoch: 1 step: 1625, loss is 0.18315211
epoch: 1 step: 1750, loss is 0.19758298
epoch: 1 step: 1875, loss is 0.0815863
测试此时的网络,可以观察到LeNet已经达到比较高的精度:
[11]:
test_net(model, mnist_path)
{'Accuracy': 0.9691}
加载已经训练好的LeNet模型:
[ ]:
param_dict = load_checkpoint("checkpoint_lenet-1_1875.ckpt")
load_param_into_net(net, param_dict)
实现FGSM
在得到精准的LeNet网络之后,下面将会采用FSGM攻击方法,在图像中加载噪声后重新进行测试。
先通过损失函数求取反向梯度:
[13]:
class WithLossCell(nn.Cell):
"""
包装网络与损失函数
"""
def __init__(self, network, loss_fn):
super(WithLossCell, self).__init__()
self._network = network
self._loss_fn = loss_fn
def construct(self, data, label):
out = self._network(data)
return self._loss_fn(out, label)
class GradWrapWithLoss(nn.Cell):
"""
通过loss求反向梯度
"""
def __init__(self, network):
super(GradWrapWithLoss, self).__init__()
self._grad_all = ops.composite.GradOperation(get_all=True, sens_param=False)
self._network = network
def construct(self, inputs, labels):
gout = self._grad_all(self._network)(inputs, labels)
return gout[0]
然后根据公式实现FGSM攻击:
[14]:
class FastGradientSignMethod:
"""
实现FGSM攻击
"""
def __init__(self, network, eps=0.07, loss_fn=None):
# 变量初始化
self._network = network
self._eps = eps
with_loss_cell = WithLossCell(self._network, loss_fn)
self._grad_all = GradWrapWithLoss(with_loss_cell)
self._grad_all.set_train()
def _gradient(self, inputs, labels):
# 求取梯度
out_grad = self._grad_all(inputs, labels)
gradient = out_grad.asnumpy()
gradient = np.sign(gradient)
return gradient
def generate(self, inputs, labels):
# 实现FGSM
inputs_tensor = Tensor(inputs)
labels_tensor = Tensor(labels)
gradient = self._gradient(inputs_tensor, labels_tensor)
# 产生扰动
perturbation = self._eps*gradient
# 生成受到扰动的图片
adv_x = inputs + perturbation
return adv_x
def batch_generate(self, inputs, labels, batch_size=32):
# 对数据集进行处理
arr_x = inputs
arr_y = labels
len_x = len(inputs)
batches = int(len_x / batch_size)
res = []
for i in range(batches):
x_batch = arr_x[i*batch_size: (i + 1)*batch_size]
y_batch = arr_y[i*batch_size: (i + 1)*batch_size]
adv_x = self.generate(x_batch, y_batch)
res.append(adv_x)
adv_x = np.concatenate(res, axis=0)
return adv_x
再次处理MINIST数据集中测试集的图片:
[15]:
images = []
labels = []
test_images = []
test_labels = []
predict_labels = []
ds_test = create_dataset(os.path.join(mnist_path, "test"), batch_size=32).create_dict_iterator(output_numpy=True)
for data in ds_test:
images = data['image'].astype(np.float32)
labels = data['label']
test_images.append(images)
test_labels.append(labels)
pred_labels = np.argmax(model.predict(Tensor(images)).asnumpy(), axis=1)
predict_labels.append(pred_labels)
test_images = np.concatenate(test_images)
predict_labels = np.concatenate(predict_labels)
true_labels = np.concatenate(test_labels)
运行攻击
由FGSM攻击公式中可以看出,攻击系数\(\varepsilon\)越大,对梯度的改变就越大。当\(\varepsilon\) 为零时则攻击效果不体现。
\(\eta = \varepsilon sign(\nabla_x J(\theta))\)
现在先观察当\(\varepsilon\)为零时的攻击效果:
[16]:
fgsm = FastGradientSignMethod(net, eps=0.0, loss_fn=net_loss)
advs = fgsm.batch_generate(test_images, true_labels, batch_size=32)
adv_predicts = model.predict(Tensor(advs)).asnumpy()
adv_predicts = np.argmax(adv_predicts, axis=1)
accuracy = np.mean(np.equal(adv_predicts, true_labels))
print(accuracy)
0.9602363782051282
再将\(\varepsilon\)设定为0.5,尝试运行攻击:
[17]:
fgsm = FastGradientSignMethod(net, eps=0.5, loss_fn=net_loss)
advs = fgsm.batch_generate(test_images, true_labels, batch_size=32)
adv_predicts = model.predict(Tensor(advs)).asnumpy()
adv_predicts = np.argmax(adv_predicts, axis=1)
accuracy = np.mean(np.equal(adv_predicts, true_labels))
print(accuracy)
0.3212139423076923
此时LeNet模型的精度大幅降低。
下面演示受攻击照片现在的实际形态,可以看出图片并没有发生明显的改变,然而在精度测试中却有了不一样的结果:
[18]:
import matplotlib.pyplot as plt
adv_examples = np.transpose(advs[:10], [0, 2, 3, 1])
ori_examples = np.transpose(test_images[:10], [0, 2, 3, 1])
plt.figure()
for i in range(10):
plt.subplot(2, 10, i+1)
plt.imshow(np.squeeze(ori_examples[i]))
plt.subplot(2, 10, i+11)
plt.imshow(np.squeeze(adv_examples[i]))
plt.show()