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