English

Improved Image Wasserstein Attacks and Defenses

Machine Learning 2023-05-11 v2 Cryptography and Security Machine Learning

Abstract

Robustness against image perturbations bounded by a p\ell_p ball have been well-studied in recent literature. Perturbations in the real-world, however, rarely exhibit the pixel independence that p\ell_p threat models assume. A recently proposed Wasserstein distance-bounded threat model is a promising alternative that limits the perturbation to pixel mass movements. We point out and rectify flaws in previous definition of the Wasserstein threat model and explore stronger attacks and defenses under our better-defined framework. Lastly, we discuss the inability of current Wasserstein-robust models in defending against perturbations seen in the real world. Our code and trained models are available at https://github.com/edwardjhu/improved_wasserstein .

Cite

@article{arxiv.2004.12478,
  title  = {Improved Image Wasserstein Attacks and Defenses},
  author = {Edward J. Hu and Adith Swaminathan and Hadi Salman and Greg Yang},
  journal= {arXiv preprint arXiv:2004.12478},
  year   = {2023}
}

Comments

Best paper award at ICLR Trustworthy ML Workshop 2020

R2 v1 2026-06-23T15:06:31.929Z