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Adversarial Attack and Defense for Non-Parametric Two-Sample Tests. (arXiv:2202.03077v2 [cs.LG] UPDATED)
June 20, 2022, 1:20 a.m. | Xilie Xu, Jingfeng Zhang, Feng Liu, Masashi Sugiyama, Mohan Kankanhalli
cs.CR updates on arXiv.org arxiv.org
Non-parametric two-sample tests (TSTs) that judge whether two sets of samples
are drawn from the same distribution, have been widely used in the analysis of
critical data. People tend to employ TSTs as trusted basic tools and rarely
have any doubt about their reliability. This paper systematically uncovers the
failure mode of non-parametric TSTs through adversarial attacks and then
proposes corresponding defense strategies. First, we theoretically show that an
adversary can upper-bound the distributional shift which guarantees the
attack's invisibility. …
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