FGSM、PGD、BIM对抗攻击算法实现

本篇文章是博主在AI、无人机、强化学习等领域学习时，用于个人学习、研究或者欣赏使用，并基于博主对人工智能等领域的一些理解而记录的学习摘录和笔记，若有不当和侵权之处，指出后将会立即改正，还望谅解。文章分类在AI学习：

AI学习笔记（6）---《FGSM、PGD、BIM对抗攻击算法实现》

FGSM、PGD、BIM对抗攻击算法实现

1 前言

2 采用PGD对抗样本生成

3 采用BIM对抗样本生成

4 总结

1 前言

PGD、BIM对抗攻击算法实现可以直接导入这个 torchattacks这个库，这个库中有很多常用的对抗攻击的算法。

pip install  torchattacks

然后添加相关代码，即可直接调用：

perturbed_data = torchattacks.PGD(model, epsilon, 0.2, steps=4)
# perturbed_data = (torchattacks.BIM(model, epsilon, 0.2, steps=4))
perturbed_data = perturbed_data(data, target)

FGSM对抗样本识别的代码请看这篇文章：

FGSM对抗攻击算法实现

本篇文章主要分享使用FGSM、PGD、BIM对抗攻击算法实现实现手写数字识别，项目的代码在下面的链接中：

FGSM、PGD、BIM对抗攻击算法实现资源

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2 采用PGD对抗样本生成

2.1 PGD对抗样本生成代码

class PGD(Attack):
    def __init__(self, model, eps=8 / 255, alpha=2 / 255, steps=10, random_start=True):
        super().__init__("PGD", model)
        self.eps = eps
        self.alpha = alpha
        self.steps = steps
        self.random_start = random_start
        self.supported_mode = ["default", "targeted"]

    def forward(self, images, labels):
        r"""
        Overridden.
        """

        images = images.clone().detach().to(self.device)
        labels = labels.clone().detach().to(self.device)

        if self.targeted:
            target_labels = self.get_target_label(images, labels)

        loss = nn.CrossEntropyLoss()
        adv_images = images.clone().detach()

        if self.random_start:
            # Starting at a uniformly random point
            adv_images = adv_images + torch.empty_like(adv_images).uniform_(
                -self.eps, self.eps
            )
            adv_images = torch.clamp(adv_images, min=0, max=1).detach()

        for _ in range(self.steps):
            adv_images.requires_grad = True
            outputs = self.get_logits(adv_images)

            # Calculate loss
            if self.targeted:
                cost = -loss(outputs, target_labels)
            else:
                cost = loss(outputs, labels)

            # Update adversarial images
            grad = torch.autograd.grad(
                cost, adv_images, retain_graph=False, create_graph=False
            )[0]

            adv_images = adv_images.detach() + self.alpha * grad.sign()
            delta = torch.clamp(adv_images - images, min=-self.eps, max=self.eps)
            adv_images = torch.clamp(images + delta, min=0, max=1).detach()

        return adv_images

2.2 PGD对抗样本生成测试结果

3 采用BIM对抗样本生成

3.1 BIM对抗样本生成代码

class BIM(Attack):

    def __init__(self, model, eps=8 / 255, alpha=2 / 255, steps=10):
        super().__init__("BIM", model)
        self.eps = eps
        self.alpha = alpha
        if steps == 0:
            self.steps = int(min(eps * 255 + 4, 1.25 * eps * 255))
        else:
            self.steps = steps
        self.supported_mode = ["default", "targeted"]

    def forward(self, images, labels):
        r"""
        Overridden.
        """

        images = images.clone().detach().to(self.device)
        labels = labels.clone().detach().to(self.device)

        if self.targeted:
            target_labels = self.get_target_label(images, labels)

        loss = nn.CrossEntropyLoss()

        ori_images = images.clone().detach()

        for _ in range(self.steps):
            images.requires_grad = True
            outputs = self.get_logits(images)

            # Calculate loss
            if self.targeted:
                cost = -loss(outputs, target_labels)
            else:
                cost = loss(outputs, labels)

            # Update adversarial images
            grad = torch.autograd.grad(
                cost, images, retain_graph=False, create_graph=False
            )[0]

            adv_images = images + self.alpha * grad.sign()
            a = torch.clamp(ori_images - self.eps, min=0)
            b = (adv_images >= a).float() * adv_images + (
                adv_images < a
            ).float() * a  # nopep8
            c = (b > ori_images + self.eps).float() * (ori_images + self.eps) + (
                b <= ori_images + self.eps
            ).float() * b  # nopep8
            images = torch.clamp(c, max=1).detach()

        return images