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

09/22/2021
by   Mikhail Pautov, et al.
0

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 ℓ_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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset