Certified Adversarial Robustness Under the Bounded Support Set

Yiwen Kou, Qinyuan Zheng, Yisen Wang
Proceedings of the 39th International Conference on Machine Learning, PMLR 162:11559-11597, 2022.

Abstract

Deep neural networks (DNNs) have revealed severe vulnerability to adversarial perturbations, beside empirical adversarial training for robustness, the design of provably robust classifiers attracts more and more attention. Randomized smoothing methods provide the certified robustness with agnostic architecture, which is further extended to a provable robustness framework using f-divergence. While these methods cannot be applied to smoothing measures with bounded support set such as uniform probability measure due to the use of likelihood ratio in their certification methods. In this paper, we generalize the $f$-divergence-based framework to a Wasserstein-distance-based and total-variation-distance-based framework that is first able to analyze robustness properties of bounded support set smoothing measures both theoretically and experimentally. By applying our methodology to uniform probability measures with support set $l_p (p=1,2,\infty\text{ and general})$ ball, we prove negative certified robustness properties with respect to $l_q (q=1, 2, \infty)$ perturbations and present experimental results on CIFAR-10 dataset with ResNet to validate our theory. And it is also worth mentioning that our certification procedure only costs constant computation time.

Cite this Paper

BibTeX
@InProceedings{pmlr-v162-kou22a, title = {Certified Adversarial Robustness Under the Bounded Support Set}, author = {Kou, Yiwen and Zheng, Qinyuan and Wang, Yisen}, booktitle = {Proceedings of the 39th International Conference on Machine Learning}, pages = {11559--11597}, year = {2022}, editor = {Chaudhuri, Kamalika and Jegelka, Stefanie and Song, Le and Szepesvari, Csaba and Niu, Gang and Sabato, Sivan}, volume = {162}, series = {Proceedings of Machine Learning Research}, month = {17--23 Jul}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v162/kou22a/kou22a.pdf}, url = {https://proceedings.mlr.press/v162/kou22a.html}, abstract = {Deep neural networks (DNNs) have revealed severe vulnerability to adversarial perturbations, beside empirical adversarial training for robustness, the design of provably robust classifiers attracts more and more attention. Randomized smoothing methods provide the certified robustness with agnostic architecture, which is further extended to a provable robustness framework using f-divergence. While these methods cannot be applied to smoothing measures with bounded support set such as uniform probability measure due to the use of likelihood ratio in their certification methods. In this paper, we generalize the $f$-divergence-based framework to a Wasserstein-distance-based and total-variation-distance-based framework that is first able to analyze robustness properties of bounded support set smoothing measures both theoretically and experimentally. By applying our methodology to uniform probability measures with support set $l_p (p=1,2,\infty\text{ and general})$ ball, we prove negative certified robustness properties with respect to $l_q (q=1, 2, \infty)$ perturbations and present experimental results on CIFAR-10 dataset with ResNet to validate our theory. And it is also worth mentioning that our certification procedure only costs constant computation time.} }
Endnote
%0 Conference Paper %T Certified Adversarial Robustness Under the Bounded Support Set %A Yiwen Kou %A Qinyuan Zheng %A Yisen Wang %B Proceedings of the 39th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2022 %E Kamalika Chaudhuri %E Stefanie Jegelka %E Le Song %E Csaba Szepesvari %E Gang Niu %E Sivan Sabato %F pmlr-v162-kou22a %I PMLR %P 11559--11597 %U https://proceedings.mlr.press/v162/kou22a.html %V 162 %X Deep neural networks (DNNs) have revealed severe vulnerability to adversarial perturbations, beside empirical adversarial training for robustness, the design of provably robust classifiers attracts more and more attention. Randomized smoothing methods provide the certified robustness with agnostic architecture, which is further extended to a provable robustness framework using f-divergence. While these methods cannot be applied to smoothing measures with bounded support set such as uniform probability measure due to the use of likelihood ratio in their certification methods. In this paper, we generalize the $f$-divergence-based framework to a Wasserstein-distance-based and total-variation-distance-based framework that is first able to analyze robustness properties of bounded support set smoothing measures both theoretically and experimentally. By applying our methodology to uniform probability measures with support set $l_p (p=1,2,\infty\text{ and general})$ ball, we prove negative certified robustness properties with respect to $l_q (q=1, 2, \infty)$ perturbations and present experimental results on CIFAR-10 dataset with ResNet to validate our theory. And it is also worth mentioning that our certification procedure only costs constant computation time.
APA
Kou, Y., Zheng, Q. & Wang, Y.. (2022). Certified Adversarial Robustness Under the Bounded Support Set. Proceedings of the 39th International Conference on Machine Learning, in Proceedings of Machine Learning Research 162:11559-11597 Available from https://proceedings.mlr.press/v162/kou22a.html.

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