Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness

Avrim Blum, Omar Montasser, Greg Shakhnarovich, Hongyang Zhang

Keywords: boosting, adversarial robustness, sample complexity, oracle complexity

TL;DR: We present an oracle-efficient algorithm for boosting robustness to adversarial examples.

Abstract: We present an oracle-efficient algorithm for boosting the adversarial robustness of barely robust learners. Barely robust learning algorithms learn predictors that are adversarially robust only on a small fraction $\beta \ll 1$ of the data distribution. Our proposed notion of barely robust learning requires robustness with respect to a ``larger'' perturbation set; which we show is necessary for strongly robust learning, and that weaker relaxations are not sufficient for strongly robust learning. Our results reveal a qualitative and quantitative equivalence between two seemingly unrelated problems: strongly robust learning and barely robust learning.