Web: http://arxiv.org/abs/2211.12713

Nov. 24, 2022, 2:10 a.m. | Shengcai Liu, Fu Peng, Ke Tang

cs.CR updates on arXiv.org arxiv.org

Attack Ensemble (AE), which combines multiple attacks together, provides a
reliable way to evaluate adversarial robustness. In practice, AEs are often
constructed and tuned by human experts, which however tends to be sub-optimal
and time-consuming. In this work, we present AutoAE, a conceptually simple
approach for automatically constructing AEs. In brief, AutoAE repeatedly adds
the attack and its iteration steps to the ensemble that maximizes ensemble
improvement per additional iteration consumed. We show theoretically that
AutoAE yields AEs provably within …

attack robustness

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