adversarial-attacks-pytorch
Torchattacks provides a comprehensive PyTorch library for the generation of adversarial attacks, integrating easily for those working with PyTorch. Supporting attack methods such as FGSM, PGD, and CW, the library offers a flexible interface to create adversarial examples efficiently. Detailed documentation and compatibility with PyTorch models position Torchattacks as a critical tool for adversarial research. It includes features like targeted attack modes, batch processing, and compatibility with frameworks like MAIR and RobustBench. Users can explore demonstrations and achieve notable success in adversarial example development.
