SMART/adversarial-attacks-pytorch/torchattacks/attacks/pgdl2.py

import torch
import torch.nn as nn

from ..attack import Attack


class PGDL2(Attack):
    r"""
    PGD in the paper 'Towards Deep Learning Models Resistant to Adversarial Attacks'
    [https://arxiv.org/abs/1706.06083]

    Distance Measure : L2

    Arguments:
        model (nn.Module): model to attack.
        eps (float): maximum perturbation. (Default: 1.0)
        alpha (float): step size. (Default: 0.2)
        steps (int): number of steps. (Default: 10)
        random_start (bool): using random initialization of delta. (Default: True)

    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].
        - labels: :math:`(N)` where each value :math:`y_i` is :math:`0 \leq y_i \leq` `number of labels`.
        - output: :math:`(N, C, H, W)`.

    Examples::
        >>> attack = torchattacks.PGDL2(model, eps=1.0, alpha=0.2, steps=10, random_start=True)
        >>> adv_images = attack(images, labels)

    """

    def __init__(
        self,
        model,
        eps=1.0,
        alpha=0.2,
        steps=10,
        random_start=True,
        eps_for_division=1e-10,
    ):
        super().__init__("PGDL2", model)
        self.eps = eps
        self.alpha = alpha
        self.steps = steps
        self.random_start = random_start
        self.eps_for_division = eps_for_division
        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()
        batch_size = len(images)

        if self.random_start:
            # Starting at a uniformly random point
            delta = torch.empty_like(adv_images).normal_()
            d_flat = delta.view(adv_images.size(0), -1)
            n = d_flat.norm(p=2, dim=1).view(adv_images.size(0), 1, 1, 1)
            r = torch.zeros_like(n).uniform_(0, 1)
            delta *= r / n * self.eps
            adv_images = torch.clamp(adv_images + delta, 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]
            grad_norms = (
                torch.norm(grad.view(batch_size, -1), p=2, dim=1)
                + self.eps_for_division
            )  # nopep8
            grad = grad / grad_norms.view(batch_size, 1, 1, 1)
            adv_images = adv_images.detach() + self.alpha * grad

            delta = adv_images - images
            delta_norms = torch.norm(delta.view(batch_size, -1), p=2, dim=1)
            factor = self.eps / delta_norms
            factor = torch.min(factor, torch.ones_like(delta_norms))
            delta = delta * factor.view(-1, 1, 1, 1)

            adv_images = torch.clamp(images + delta, min=0, max=1).detach()

        return adv_images