25 citations · 95 across the 13 of their papers we have counts for
5 papers · 1 filter
Unadversarial Examples: Designing Objects for Robust Vision
Hadi Salman, Andrew Ilyas, Logan Engstrom +3
We study a class of realistic computer vision settings wherein one can influence the design of the objects being recognized. We develop a framework that leverages this capability t…
Do Adversarially Robust ImageNet Models Transfer Better?
Hadi Salman, Andrew Ilyas, Logan Engstrom +2
Transfer learning is a widely-used paradigm in deep learning, where models pre-trained on standard datasets can be efficiently adapted to downstream tasks. Typically, better pre-tr…
Improved Image Wasserstein Attacks and Defenses
Edward J. Hu, Adith Swaminathan, Hadi Salman +1
Robustness against image perturbations bounded by a ball have been well-studied in recent literature. Perturbations in the real-world, however, rarely exhibit the pixel in…
Denoised Smoothing: A Provable Defense for Pretrained Classifiers
Hadi Salman, Mingjie Sun, Greg Yang +2
We present a method for provably defending any pretrained image classifier against adversarial attacks. This method, for instance, allows public vision API providers and u…
Randomized Smoothing of All Shapes and Sizes
Greg Yang, Tony Duan, J. Edward Hu +3
Randomized smoothing is the current state-of-the-art defense with provable robustness against adversarial attacks. Many works have devised new randomized smoothing schemes…