生成一段基于CIFAR模型进行PGD攻击算法的代码
时间: 2023-12-15 20:05:29 浏览: 42
以下是基于CIFAR模型进行PGD攻击的代码示例:
```python
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms
from torch.autograd import Variable
# Define CIFAR model
class CIFARModel(nn.Module):
def __init__(self):
super(CIFARModel, self).__init__()
self.conv1 = nn.Conv2d(3, 32, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.fc1 = nn.Linear(4*4*128, 512)
self.fc2 = nn.Linear(512, 10)
def forward(self, x):
x = nn.functional.relu(self.conv1(x))
x = nn.functional.relu(self.conv2(x))
x = nn.functional.relu(self.conv3(x))
x = x.view(-1, 4*4*128)
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return x
# Define PGD attack function
def pgd_attack(model, X, y, epsilon, alpha, num_iter):
delta = torch.zeros_like(X, requires_grad=True)
for t in range(num_iter):
loss = nn.CrossEntropyLoss()(model(X + delta), y)
loss.backward()
delta.data = (delta + alpha*delta.grad.detach().sign()).clamp(-epsilon,epsilon)
delta.grad.zero_()
return delta.detach()
# Load CIFAR dataset
transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.5,0.5,0.5),(0.5,0.5,0.5))])
trainset = datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
testset = datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
test_loader = torch.utils.data.DataLoader(testset, batch_size=128, shuffle=False, num_workers=2)
# Define model, optimizer and loss function
model = CIFARModel()
optimizer = optim.Adam(model.parameters(), lr=0.001)
criterion = nn.CrossEntropyLoss()
# Train model
for epoch in range(10):
for i, (X, y) in enumerate(train_loader):
model.train()
optimizer.zero_grad()
X, y = Variable(X), Variable(y)
output = model(X)
loss = criterion(output, y)
loss.backward()
optimizer.step()
# Evaluate model on clean and adversarial examples
model.eval()
correct = 0
total = 0
for X, y in test_loader:
X, y = Variable(X), Variable(y)
output = model(X)
_, predicted = torch.max(output.data, 1)
total += y.size(0)
correct += (predicted == y).sum().item()
print('Accuracy on clean examples: %d %%' % (100 * correct / total))
correct = 0
total = 0
for X, y in test_loader:
X, y = Variable(X), Variable(y)
delta = pgd_attack(model, X, y, epsilon=0.031, alpha=0.008, num_iter=10)
X_adv = Variable(X + delta)
output = model(X_adv)
_, predicted = torch.max(output.data, 1)
total += y.size(0)
correct += (predicted == y).sum().item()
print('Accuracy on adversarial examples: %d %%' % (100 * correct / total))
```
这段代码实现了一个基于CIFAR模型的PGD攻击算法,包括定义模型、PGD攻击函数、加载CIFAR数据集、定义模型优化器和损失函数、训练模型、以及在干净和对抗样本上评估模型的准确率。