有现成的库cleverhans可以调用
https://github.com/cleverhans-lab/cleverhans
这个库是用tensorflow实现的，但也实现了pytorch版本
因为我调用的是pytorch版本，我接下来大概讲一下如何使用
他们给出了一个调用pytorch版本的例子：
https://github.com/cleverhans-lab/cleverhans/blob/master/tutorials/torch/cifar10_tutorial.py
代码如下：
from absl import app, flags
from easydict import EasyDict
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvisionfrom cleverhans.torch.attacks.fast_gradient_method import fast_gradient_method
from cleverhans.torch.attacks.projected_gradient_descent import projected_gradient_descentFLAGS = flags.FLAGSclass CNN(torch.nn.Module):"""Basic CNN architecture."""def __init__(self, in_channels=1):super(CNN, self).__init__()self.conv1 = nn.Conv2d(in_channels, 64, 8, 1)self.conv2 = nn.Conv2d(64, 128, 6, 2)self.conv3 = nn.Conv2d(128, 128, 5, 2)self.fc = nn.Linear(128*3*3, 10)def forward(self, x):x = F.relu(self.conv1(x))x = F.relu(self.conv2(x))x = F.relu(self.conv3(x))x = x.view(-1, 128*3*3)x = self.fc(x)return xdef ld_cifar10():"""Load training and test data."""train_transforms = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])test_transforms = torchvision.transforms.Compose([torchvision.transforms.ToTensor()])train_dataset = torchvision.datasets.CIFAR10(root='/tmp/data', train=True, transform=train_transforms, download=True)test_dataset = torchvision.datasets.CIFAR10(root='/tmp/data', train=False, transform=test_transforms, download=True)train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=128, shuffle=True, num_workers=2)test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=128, shuffle=False, num_workers=2)return EasyDict(train=train_loader, test=test_loader)def main(_):# Load training and test datadata = ld_cifar10()# Instantiate model, loss, and optimizer for trainingnet = CNN(in_channels=3)device = 'cuda' if torch.cuda.is_available() else 'cpu'if device == 'cuda':net = net.cuda()loss_fn = torch.nn.CrossEntropyLoss(reduction='mean')optimizer = torch.optim.Adam(net.parameters(), lr=1e-3)# Train vanilla modelnet.train()for epoch in range(1, FLAGS.nb_epochs + 1):train_loss = 0.for x, y in data.train:x, y = x.to(device), y.to(device)if FLAGS.adv_train:# Replace clean example with adversarial example for adversarial trainingx = projected_gradient_descent(net, x, FLAGS.eps, 0.01, 40, np.inf)optimizer.zero_grad()loss = loss_fn(net(x), y)loss.backward()optimizer.step()train_loss += loss.item()print('epoch: {}/{}, train loss: {:.3f}'.format(epoch, FLAGS.nb_epochs, train_loss))# Evaluate on clean and adversarial datanet.eval()report = EasyDict(nb_test=0, correct=0, correct_fgm=0, correct_pgd=0)for x, y in data.test:x, y = x.to(device), y.to(device)x_fgm = fast_gradient_method(net, x, FLAGS.eps, np.inf)x_pgd = projected_gradient_descent(net, x, FLAGS.eps, 0.01, 40, np.inf)_, y_pred = net(x).max(1) # model prediction on clean examples_, y_pred_fgm = net(x_fgm).max(1) # model prediction on FGM adversarial examples_, y_pred_pgd = net(x_pgd).max(1) # model prediction on PGD adversarial examplesreport.nb_test += y.size(0)report.correct += y_pred.eq(y).sum().item()report.correct_fgm += y_pred_fgm.eq(y).sum().item()report.correct_pgd += y_pred_pgd.eq(y).sum().item()print('test acc on clean examples (%): {:.3f}'.format(report.correct / report.nb_test * 100.))print('test acc on FGM adversarial examples (%): {:.3f}'.format(report.correct_fgm / report.nb_test * 100.))print('test acc on PGD adversarial examples (%): {:.3f}'.format(report.correct_pgd / report.nb_test * 100.))if __name__ == '__main__':flags.DEFINE_integer('nb_epochs', 8, 'Number of epochs.')flags.DEFINE_float('eps', 0.3, 'Total epsilon for FGM and PGD attacks.')flags.DEFINE_bool('adv_train', False, 'Use adversarial training (on PGD adversarial examples).')app.run(main)代码不细讲，关键点在于下面这句，通过这句利用FGSM生成对抗样本
x_fgm = fast_gradient_method(net, x, FLAGS.eps, np.inf)有一点需要注意，上述代码中需要引入了：
from cleverhans.torch.attacks.fast_gradient_method import fast_gradient_methodgithub的readme中中说首先需要安装
pip install cleverhans我确实可以安装这个库，导入也没有问题，但 执行到下面这句就是不通过
from cleverhans.torch.attacks.fast_gradient_method import fast_gradient_method说cleverhans.torch不存在？
于是没办法我只能把github上实现的代码复制下来，自己建一个一模一样的fast_gradient_method方法
