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Multi-armed adversarial attacks, in which multiple algorithms and objectiveloss functions are simultaneously used at evaluation time, have been shown tobe highly successful in fooling state-of-the-art adversarial examples detectorswhile requiring no specific side information about the detection mechanism. Byformalizing the problem at hand, we can propose a solution that aggregates thesoft-probability outputs of multiple pre-trained detectors according to aminimax approach. The proposed framework is mathematically sound, easy toimplement, and modular, allowing for integrating existing or future detectors.Through extensive evaluation on popular datasets (e.g., CIFAR10 and SVHN), weshow that our aggregation consistently outperforms individual state-of-the-artdetectors against multi-armed adversarial attacks, making it an effectivesolution to improve the resilience of available methods.

A Minimax Approach Against Multi-Armed Adversarial Attacks Detection