Playing the Game of Universal Adversarial Perturbations
This work addresses the challenge of adversarial attacks in machine learning, which is critical for security in applications like image classification, but it is incremental as it builds on existing game-theoretic methods.
The authors tackled the problem of making classifiers robust to universal adversarial perturbations by framing it as a two-player zero-sum game, and they empirically demonstrated robustness in defense scenarios on CIFAR10, CIFAR100, and ImageNet datasets.
We study the problem of learning classifiers robust to universal adversarial perturbations. While prior work approaches this problem via robust optimization, adversarial training, or input transformation, we instead phrase it as a two-player zero-sum game. In this new formulation, both players simultaneously play the same game, where one player chooses a classifier that minimizes a classification loss whilst the other player creates an adversarial perturbation that increases the same loss when applied to every sample in the training set. By observing that performing a classification (respectively creating adversarial samples) is the best response to the other player, we propose a novel extension of a game-theoretic algorithm, namely fictitious play, to the domain of training robust classifiers. Finally, we empirically show the robustness and versatility of our approach in two defence scenarios where universal attacks are performed on several image classification datasets -- CIFAR10, CIFAR100 and ImageNet.