import torch
from ..attack import Attack
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class GN(Attack):
r"""
Add Gaussian Noise.
Arguments:
model (nn.Module): model to attack.
std (nn.Module): standard deviation (Default: 0.1).
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.GN(model)
>>> adv_images = attack(images, labels)
"""
def __init__(self, model, std=0.1):
super().__init__("GN", model)
self.std = std
self.supported_mode = ["default"]
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def forward(self, images, labels=None):
r"""
Overridden.
"""
images = images.clone().detach().to(self.device)
adv_images = images + self.std * torch.randn_like(images)
adv_images = torch.clamp(adv_images, min=0, max=1).detach()
return adv_images