Certified Robustness to Adversarial Examples with Differential Privacy - Lecuyer, Mathias; Atlidakis, Vaggelis; Geambasu, Roxana; Hsu, Daniel; Jana, Suman (S&P ’19, 2019)
Adversarial examples that fool machine learning models, particularly deep neural networks, have been a topic of intense research interest, with attacks and defenses being developed in a tight back-and-forth. Most past defenses are best effort and have been shown to be vulnerable to sophisticated attacks. Recently a set of certiﬁed defenses have been introduced, which provide guarantees of robustness to norm-bounded attacks. However these defenses either do not scale to large datasets or are limited in the types of models they can support. This paper presents the ﬁrst certiﬁed defense that both scales to large networks and datasets (such as Google’s Inception network for ImageNet) and applies broadly to arbitrary model types. Our defense, called PixelDP, is based on a novel connection between robustness against adversarial examples and differential privacy, a cryptographically-inspired privacy formalism, that provides a rigorous, generic, and ﬂexible foundation for defense.
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