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Not all broken defenses are equal: The dead angles of adversarial accuracy. (arXiv:2207.04129v1 [cs.LG])
July 12, 2022, 1:20 a.m. | Raphael Olivier, Bhiksha Raj
cs.CR updates on arXiv.org arxiv.org
Robustness to adversarial attack is typically evaluated with adversarial
accuracy. This metric is however too coarse to properly capture all robustness
properties of machine learning models. Many defenses, when evaluated against a
strong attack, do not provide accuracy improvements while still contributing
partially to adversarial robustness. Popular certification methods suffer from
the same issue, as they provide a lower bound to accuracy. To capture finer
robustness properties we propose a new metric for L2 robustness, adversarial
angular sparsity, which partially …
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