| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
|
|
| from ..attack import Attack |
|
|
|
|
| class TPGD(Attack): |
| r""" |
| PGD based on KL-Divergence loss in the paper 'Theoretically Principled Trade-off between Robustness and Accuracy' |
| [https://arxiv.org/abs/1901.08573] |
| |
| Distance Measure : Linf |
| |
| Arguments: |
| model (nn.Module): model to attack. |
| eps (float): strength of the attack or maximum perturbation. (Default: 8/255) |
| alpha (float): step size. (Default: 2/255) |
| steps (int): number of steps. (Default: 10) |
| |
| Shape: |
| - images: :math:`(N, C, H, W)` where `N = number of batches`, `C = number of channels`, `H = height` and `W = width`. It must have a range [0, 1]. |
| - output: :math:`(N, C, H, W)`. |
| |
| Examples:: |
| >>> attack = torchattacks.TPGD(model, eps=8/255, alpha=2/255, steps=10) |
| >>> adv_images = attack(images) |
| |
| """ |
|
|
| def __init__(self, model, eps=8 / 255, alpha=2 / 255, steps=10): |
| super().__init__("TPGD", model) |
| self.eps = eps |
| self.alpha = alpha |
| self.steps = steps |
| self.supported_mode = ["default"] |
|
|
| def forward(self, images, labels=None): |
| r""" |
| Overridden. |
| """ |
|
|
| images = images.clone().detach().to(self.device) |
| logit_ori = self.get_logits(images).detach() |
|
|
| adv_images = images + 0.001 * torch.randn_like(images) |
| adv_images = torch.clamp(adv_images, min=0, max=1).detach() |
|
|
| loss = nn.KLDivLoss(reduction="sum") |
|
|
| for _ in range(self.steps): |
| adv_images.requires_grad = True |
| logit_adv = self.get_logits(adv_images) |
|
|
| |
| cost = loss(F.log_softmax(logit_adv, dim=1), F.softmax(logit_ori, dim=1)) |
|
|
| |
| 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 |
|
|
