PGD攻击函数的代码
时间: 2023-12-04 20:04:15 浏览: 56
以下是一个使用PyTorch实现的PGD攻击函数示例代码:
```python
import torch
import torch.nn.functional as F
def pgd_attack(model, images, labels, eps=0.3, alpha=2/255, iters=40):
"""
:param model: 需要攻击的模型
:param images: 输入的图片
:param labels: 图片对应的真实标签
:param eps: 扰动的最大范围
:param alpha: 梯度上升的步长
:param iters: 迭代次数
:return: 返回有扰动的图片
"""
loss_fn = torch.nn.CrossEntropyLoss()
ori_images = images.clone().detach()
adv_images = images.clone().detach()
for i in range(iters):
adv_images.requires_grad = True
outputs = model(adv_images)
loss = loss_fn(outputs, labels)
loss.backward()
# 对输入图片进行梯度上升
gradient = adv_images.grad.detach()
adv_images = adv_images + alpha * gradient.sign()
adv_images = torch.max(torch.min(adv_images, ori_images + eps), ori_images - eps)
adv_images = torch.clamp(adv_images, 0.0, 1.0)
adv_images = adv_images.detach()
return adv_images
```
其中,eps代表扰动的最大范围,alpha代表梯度上升的步长,iters代表迭代次数,在每次迭代中,对输入图片进行梯度上升,使其受到一定程度的扰动,从而误导模型产生错误的分类结果。