Randomized Smoothing under Attack: How Good is it in Pratice?. (arXiv:2204.14187v1 [cs.CR])

Randomized smoothing is a recent and celebrated solution to certify the
robustness of any classifier. While it indeed provides a theoretical robustness
against adversarial attacks, the dimensionality of current classifiers
necessarily imposes Monte Carlo approaches for its application in practice.
This paper questions the effectiveness of randomized smoothing as a defense,
against state of the art black-box attacks. This is a novel perspective, as
previous research works considered the certification as an unquestionable
guarantee. We first formally highlight the mismatch …

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