Provable Adversarial Robustness for Fractional Lp Threat Models

Alexander J. Levine, Soheil Feizi
Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, PMLR 151:9908-9942, 2022.

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

Cite this Paper

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
Endnote
%0 Conference Paper %T Provable Adversarial Robustness for Fractional Lp Threat Models %A Alexander J. Levine %A Soheil Feizi %B Proceedings of The 25th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2022 %E Gustau Camps-Valls %E Francisco J. R. Ruiz %E Isabel Valera %F pmlr-v151-levine22a %I PMLR %P 9908--9942 %U https://proceedings.mlr.press/v151/levine22a.html %V 151 %X 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
APA
Levine, A.J. & Feizi, S.. (2022). Provable Adversarial Robustness for Fractional Lp Threat Models . Proceedings of The 25th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 151:9908-9942 Available from https://proceedings.mlr.press/v151/levine22a.html.

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