Title: Are adversarial examples inevitable?. Abstract: A wide range of defenses have been proposed to harden neural networks against adversarial attacks. However, a pattern has emerged in which the majority of adversarial defenses are quickly broken by new attacks.  Given the lack of success at generating robust defenses, we are led to ask a fundamental question:  Are adversarial attacks inevitable?
This paper analyzes adversarial examples from a theoretical perspective, and identifies fundamental bounds on the susceptibility of a classifier to adversarial attacks.   We show that, for certain classes of problems, adversarial examples are inescapable.  Using experiments, we explore the implications of theoretical guarantees for real-world problems and discuss how factors such as dimensionality and image complexity limit a classifier's robustness against adversarial examples.

