This paper explores a scenario in which a malicious actor employs a
multi-armed attack strategy to manipulate data samples, offering them various
avenues to introduce noise into the dataset. Our central objective is to
protect the data by detecting any alterations to the input. We approach this
defensive strategy with utmost caution, operating in an environment where the
defender possesses significantly less information compared to the attacker.
Specifically, the defender is unable to utilize any data samples for training a
defense model or verifying the integrity of the channel. Instead, the defender
relies exclusively on a set of pre-existing detectors readily available ``off
the shelf''. To tackle this challenge, we derive an innovative
information-theoretic defense approach that optimally aggregates the decisions
made by these detectors, eliminating the need for any training data. We further
explore a practical use-case scenario for empirical evaluation, where the
attacker possesses a pre-trained classifier and launches well-known adversarial
attacks against it. Our experiments highlight the effectiveness of our proposed
solution, even in scenarios that deviate from the optimal setup.

在一个恶意行为者使用多臂攻击策略操纵数据样本的情景中，本文的中心目标是通过检测输入的任何改动来保护数据。我们采用了一种信息理论的防御方法，通过最优地汇总多个探测器的决策，消除了对训练数据的需求，并且在实验中展示了我们提出的解决方案的有效性。