69 citations · 222 across the 18 of their papers we have counts for
6 papers · 1 filter
Certifying Confidence via Randomized Smoothing
Aounon Kumar, Alexander Levine, Soheil Feizi +1
Randomized smoothing has been shown to provide good certified-robustness guarantees for high-dimensional classification problems. It uses the probabilities of predicting the top tw…
Tight Second-Order Certificates for Randomized Smoothing
Alexander Levine, Aounon Kumar, Thomas Goldstein +1
Randomized smoothing is a popular way of providing robustness guarantees against adversarial attacks: randomly-smoothed functions have a universal Lipschitz-like bound, allowing fo…
Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks
Wei-An Lin, Chun Pong Lau, Alexander Levine +2
Adversarial training is a popular defense strategy against attack threat models with bounded Lp norms. However, it often degrades the model performance on normal images and the def…
Deep Partition Aggregation: Provable Defense against General Poisoning Attacks
Alexander Levine, Soheil Feizi
Adversarial poisoning attacks distort training data in order to corrupt the test-time behavior of a classifier. A provable defense provides a certificate for each test sample, whic…
(De)Randomized Smoothing for Certifiable Defense against Patch Attacks
Alexander Levine, Soheil Feizi
Patch adversarial attacks on images, in which the attacker can distort pixels within a region of bounded size, are an important threat model since they provide a quantitative model…
Curse of Dimensionality on Randomized Smoothing for Certifiable Robustness
Aounon Kumar, Alexander Levine, Tom Goldstein +1
Randomized smoothing, using just a simple isotropic Gaussian distribution, has been shown to produce good robustness guarantees against -norm bounded adversaries. In this w…