论文标题
钥匙非参数假设检验
Keyed Non-Parametric Hypothesis Tests
论文作者
论文摘要
机器学习的最新流行要求对AI安全性有更深入的了解。迄今为止发表的众多AI威胁中，中毒目前引起了极大的关注。在中毒攻击中，对手部分篡改了用于学习在测试阶段误导分类器的数据集。 本文提出了一种防止中毒攻击的新保护策略。该技术依赖于一种称为键入的非参数假设测试的新原始性，允许在对抗条件下评估训练输入与以前学到的分布$ \ mathfrak {d} $的一致性。为此，我们使用了对手未知的秘密钥匙$κ$。 键合的非参数假设检验与经典测试不同，因为$κ$的保密性可防止对手误导钥匙测试，以结论一个（显着的）篡改数据集属于$ \ mathfrak {d} $。
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 $κ$ unknown to the opponent. Keyed non-parametric hypothesis tests differs from classical tests in that the secrecy of $κ$ prevents the opponent from misleading the keyed test into concluding that a (significantly) tampered dataset belongs to $\mathfrak{D}$.
