PopSkipJump: Decision-Based Attack for Probabilistic Classifiers

Carl-Johann Simon-Gabriel, Noman Ahmed Sheikh, Andreas Krause
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:9712-9721, 2021.

Abstract

Most current classifiers are vulnerable to adversarial examples, small input perturbations that change the classification output. Many existing attack algorithms cover various settings, from white-box to black-box classifiers, but usually assume that the answers are deterministic and often fail when they are not. We therefore propose a new adversarial decision-based attack specifically designed for classifiers with probabilistic outputs. It is based on the HopSkipJump attack by Chen et al. (2019), a strong and query efficient decision-based attack originally designed for deterministic classifiers. Our P(robabilisticH)opSkipJump attack adapts its amount of queries to maintain HopSkipJump’s original output quality across various noise levels, while converging to its query efficiency as the noise level decreases. We test our attack on various noise models, including state-of-the-art off-the-shelf randomized defenses, and show that they offer almost no extra robustness to decision-based attacks. Code is available at https://github.com/cjsg/PopSkipJump.

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-simon-gabriel21a, title = {PopSkipJump: Decision-Based Attack for Probabilistic Classifiers}, author = {Simon-Gabriel, Carl-Johann and Sheikh, Noman Ahmed and Krause, Andreas}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {9712--9721}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/simon-gabriel21a/simon-gabriel21a.pdf}, url = {https://proceedings.mlr.press/v139/simon-gabriel21a.html}, abstract = {Most current classifiers are vulnerable to adversarial examples, small input perturbations that change the classification output. Many existing attack algorithms cover various settings, from white-box to black-box classifiers, but usually assume that the answers are deterministic and often fail when they are not. We therefore propose a new adversarial decision-based attack specifically designed for classifiers with probabilistic outputs. It is based on the HopSkipJump attack by Chen et al. (2019), a strong and query efficient decision-based attack originally designed for deterministic classifiers. Our P(robabilisticH)opSkipJump attack adapts its amount of queries to maintain HopSkipJump’s original output quality across various noise levels, while converging to its query efficiency as the noise level decreases. We test our attack on various noise models, including state-of-the-art off-the-shelf randomized defenses, and show that they offer almost no extra robustness to decision-based attacks. Code is available at https://github.com/cjsg/PopSkipJump.} }
Endnote
%0 Conference Paper %T PopSkipJump: Decision-Based Attack for Probabilistic Classifiers %A Carl-Johann Simon-Gabriel %A Noman Ahmed Sheikh %A Andreas Krause %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-simon-gabriel21a %I PMLR %P 9712--9721 %U https://proceedings.mlr.press/v139/simon-gabriel21a.html %V 139 %X Most current classifiers are vulnerable to adversarial examples, small input perturbations that change the classification output. Many existing attack algorithms cover various settings, from white-box to black-box classifiers, but usually assume that the answers are deterministic and often fail when they are not. We therefore propose a new adversarial decision-based attack specifically designed for classifiers with probabilistic outputs. It is based on the HopSkipJump attack by Chen et al. (2019), a strong and query efficient decision-based attack originally designed for deterministic classifiers. Our P(robabilisticH)opSkipJump attack adapts its amount of queries to maintain HopSkipJump’s original output quality across various noise levels, while converging to its query efficiency as the noise level decreases. We test our attack on various noise models, including state-of-the-art off-the-shelf randomized defenses, and show that they offer almost no extra robustness to decision-based attacks. Code is available at https://github.com/cjsg/PopSkipJump.
APA
Simon-Gabriel, C., Sheikh, N.A. & Krause, A.. (2021). PopSkipJump: Decision-Based Attack for Probabilistic Classifiers. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:9712-9721 Available from https://proceedings.mlr.press/v139/simon-gabriel21a.html.

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