File size: 2,737 Bytes
605f784 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 | import torch
import torch.nn as nn
from ..attack import Attack
class PGD(Attack):
r"""
PGD in the paper 'Towards Deep Learning Models Resistant to Adversarial Attacks'
[https://arxiv.org/abs/1706.06083]
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)
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.PGD(model, eps=8/255, alpha=1/255, steps=10, random_start=True)
>>> adv_images = attack(images, labels)
"""
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
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