| |
|
|
|
|
| import torch |
| import warnings |
|
|
| def gaussain_noise(images, std=8/255, device=None, verbose=True): |
|
|
| if verbose: |
| print(f"\nGN: std={std*255}\n") |
| if images.max()>1 or images.min()<0 : warnings.warn(f"GN Attack: Image values are expected to be in the range of [0,1], instead found [min,max]=[{images.min().item()} , {images.max().item()}]") |
|
|
| images = images.clone().detach().to(device) |
| adv_images = images + std*torch.randn_like(images) |
| adv_images = torch.clamp(adv_images, min=0, max=1).detach() |
|
|
| return adv_images |
|
|
