AISTATS 2022poster6 citations
Provable Adversarial Robustness for Fractional Lp Threat Models
Alexander J. Levine, Soheil Feizi
Abstract
In recent years, researchers have extensively studied adversarial robustness in a variety of threat models, including L_0, L_1, L_2, and L_infinity-norm bounded adversarial attacks. However, attacks bounded by fractional L_p "norms" (quasi-norms defined by the L_p distance with 0
BibTeX
@InProceedings{pmlr-v151-levine22a,
title = { Provable Adversarial Robustness for Fractional Lp Threat Models },
author = {Levine, Alexander J. and Feizi, Soheil},
booktitle = {Proceedings of The 25th International Conference on Artificial Intelligence and Statistics},
pages = {9908--9942},
year = {2022},
editor = {Camps-Valls, Gustau and Ruiz, Francisco J. R. and Valera, Isabel},
volume = {151},
series = {Proceedings of Machine Learning Research},
month = {28--30 Mar},
publisher = {PMLR},
pdf = {https://proceedings.mlr.press/v151/levine22a/levine22a.pdf},
url = {https://proceedings.mlr.press/v151/levine22a.html},
abstract = { In recent years, researchers have extensively studied adversarial robustness in a variety of threat models, including L_0, L_1, L_2, and L_infinity-norm bounded adversarial attacks. However, attacks bounded by fractional L_p "norms" (quasi-norms defined by the L_p distance with 0