  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.

Machine Learning;Artificial Intelligence