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arXiv · 2109.10696

CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks

Mikhail Pautov·Nurislam Tursynbek·Marina Munkhoeva·Nikita Muravev·Aleksandr Petiushko·Ivan Oseledets
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Abstract

In safety-critical machine learning applications, it is crucial to defend models against adversarial attacks -- small modifications of the input that change the predictions. Besides rigorously studied $\ell_p$-bounded additive perturbations, recently proposed semantic perturbations (e.g. rotation, translation) raise a serious concern on deploying ML systems in real-world. Therefore, it is important to provide provable guarantees for deep learning models against semantically meaningful input transformations. In this paper, we propose a new universal probabilistic certification approach based on Chernoff-Cramer bounds that can be used in general attack settings. We estimate the probability of a model to fail if the attack is sampled from a certain distribution. Our theoretical findings are supported by experimental results on different datasets.

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Mikhail Pautov, Nurislam Tursynbek, Marina Munkhoeva, Nikita Muravev, Aleksandr Petiushko, Ivan Oseledets. 2021-09-22. CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks. https://arxiv.org/abs/2109.10696

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