Aug. 24, 2022, 1:20 a.m. | Linyi Li, Tao Xie, Bo Li

cs.CR updates on arXiv.org arxiv.org

Great advances in deep neural networks (DNNs) have led to state-of-the-art
performance on a wide range of tasks. However, recent studies have shown that
DNNs are vulnerable to adversarial attacks, which have brought great concerns
when deploying these models to safety-critical applications such as autonomous
driving. Different defense approaches have been proposed against adversarial
attacks, including: a) empirical defenses, which can usually be adaptively
attacked again without providing robustness certification; and b) certifiably
robust approaches, which consist of robustness verification …

certified lg networks neural networks robustness

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