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arXiv · 2202.05920

Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness

Avrim Blum·Omar Montasser·Greg Shakhnarovich·Hongyang Zhang
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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.

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Avrim Blum, Omar Montasser, Greg Shakhnarovich, Hongyang Zhang. 2022-02-11. Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness. https://arxiv.org/abs/2202.05920

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