File size: 3,469 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 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | 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
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