Black-box Certification and Learning under Adversarial Perturbations
Abstract
We formally study the problem of classification under adversarial perturbations from a learner's perspective as well as a third-party who aims at certifying the robustness of a given black-box classifier. We analyze a PAC-type framework of semi-supervised learning and identify possibility and impossibility results for proper learning of VC-classes in this setting. We further introduce a new setting of black-box certification under limited query budget, and analyze this for various classes of predictors and perturbation. We also consider the viewpoint of a black-box adversary that aims at finding adversarial examples, showing that the existence of an adversary with polynomial query complexity can imply the existence of a sample efficient robust learner.
Keywords
Cite
@article{arxiv.2006.16520,
title = {Black-box Certification and Learning under Adversarial Perturbations},
author = {Hassan Ashtiani and Vinayak Pathak and Ruth Urner},
journal= {arXiv preprint arXiv:2006.16520},
year = {2022}
}