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
目录
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：
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：
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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_images2.2 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 images3.2 BIM对抗样本生成测试结果
4 总结
由上述的实验结果可以看出，在epsilon相同的时候有FGSM的准确率率 > PGD的准确率 > BIM的准确率衰减速率；
通过图像的倾斜角度可以观察出，FGSM的准确率衰减速率 < PGD的准确率衰减速率 < BIM的准确率衰减速率。
如果限制扰动量的大小，以使人眼不易察觉，可以通过改进对抗样本生成方法，使用不同对抗样本生成方法，提高对抗样本的对抗性。
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