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arXiv · 2005.12227

Keyed Non-Parametric Hypothesis Tests

Yao Cheng·Cheng-Kang Chu·Hsiao-Ying Lin·Marius Lombard-Platet·David Naccache
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Abstract

The recent popularity of machine learning calls for a deeper understanding of AI security. Amongst the numerous AI threats published so far, poisoning attacks currently attract considerable attention. In a poisoning attack the opponent partially tampers the dataset used for learning to mislead the classifier during the testing phase. This paper proposes a new protection strategy against poisoning attacks. The technique relies on a new primitive called keyed non-parametric hypothesis tests allowing to evaluate under adversarial conditions the training input's conformance with a previously learned distribution $\mathfrak{D}$. To do so we use a secret key $\kappa$ unknown to the opponent. Keyed non-parametric hypothesis tests differs from classical tests in that the secrecy of $\kappa$ prevents the opponent from misleading the keyed test into concluding that a (significantly) tampered dataset belongs to $\mathfrak{D}$.

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Yao Cheng, Cheng-Kang Chu, Hsiao-Ying Lin, Marius Lombard-Platet, David Naccache. 2020-05-25. Keyed Non-Parametric Hypothesis Tests. https://doi.org/10.1007/978-3-030-36938-5_39

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