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oai:ipsj.ixsq.nii.ac.jp:00218635 2025-01-19T15:05:59Z 1164:5352:10882:10963

Adversarial Attacks and Defenses for Non-Parametric Two-Sample Tests Adversarial Attacks and Defenses for Non-Parametric Two-Sample Tests Xilie, Xu Jingfeng, Zhang Feng, Liu Masashi, Sugiyama Mohan, Kankanhalli Xilie, Xu Jingfeng, Zhang Feng, Liu Masashi, Sugiyama Mohan, Kankanhalli 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 upperbound the distributional shift which guarantees the attack’s invisibility. Furthermore, we theoretically find that the adversary can also degrade the lower bound of a TST’s test power, which enables us to iteratively minimize the test criterion in order to search for adversarial pairs. To enable TST-agnostic attacks, we propose an ensemble attack (EA) framework that jointly minimizes the different types of test criteria. Second, to robustify TSTs, we propose a max-min optimization that iteratively generates adversarial pairs to train the deep kernels. Extensive experiments on both simulated and real-world datasets validate the adversarial vulnerabilities of non-parametric TSTs and the effectiveness of our proposed defense. 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 upperbound the distributional shift which guarantees the attack’s invisibility. Furthermore, we theoretically find that the adversary can also degrade the lower bound of a TST’s test power, which enables us to iteratively minimize the test criterion in order to search for adversarial pairs. To enable TST-agnostic attacks, we propose an ensemble attack (EA) framework that jointly minimizes the different types of test criteria. Second, to robustify TSTs, we propose a max-min optimization that iteratively generates adversarial pairs to train the deep kernels. Extensive experiments on both simulated and real-world datasets validate the adversarial vulnerabilities of non-parametric TSTs and the effectiveness of our proposed defense. technical report 情報処理学会 2022-06-20 application/pdf 研究報告バイオ情報学（BIO） 5 2022-BIO-70 1 27 2188-8590 AA12055912 https://ipsj.ixsq.nii.ac.jp/record/218635/files/IPSJ-BIO22070005.pdf eng