import torch
import torch.nn as nn
from ..attack import Attack
[docs]
class EOTPGD(Attack):
r"""
Comment on "Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network"
[https://arxiv.org/abs/1907.00895]
Distance Measure : Linf
Arguments:
model (nn.Module): model to attack.
eps (float): maximum perturbation. (Default: 8/255)
alpha (float): step size. (Default: 2/255)
steps (int): number of steps. (Default: 10)
eot_iter (int) : number of models to estimate the mean gradient. (Default: 2)
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.EOTPGD(model, eps=8/255, alpha=2/255, steps=10, eot_iter=2)
>>> adv_images = attack(images, labels)
"""
def __init__(
self, model, eps=8 / 255, alpha=2 / 255, steps=10, eot_iter=2, random_start=True
):
super().__init__("EOTPGD", model)
self.eps = eps
self.alpha = alpha
self.steps = steps
self.eot_iter = eot_iter
self.random_start = random_start
self.supported_mode = ["default", "targeted"]
[docs]
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
) # nopep8
adv_images = torch.clamp(adv_images, min=0, max=1).detach()
for _ in range(self.steps):
grad = torch.zeros_like(adv_images)
adv_images.requires_grad = True
for j in range(self.eot_iter):
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/self.eot_iter).sign() == grad.sign()
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