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Alongside the well-publicized accomplishments of deep neural networks therehas emerged an apparent bug in their success on tasks such as objectrecognition: with deep models trained using vanilla methods, input images canbe slightly corrupted in order to modify output predictions, even when thesecorruptions are practically invisible. This apparent lack of robustness has ledresearchers to propose methods that can help to prevent an adversary fromhaving such capabilities. The state-of-the-art approaches have incorporated therobustness requirement into the loss function, and the training processinvolves taking stochastic gradient descent steps not using original inputs buton adversarially-corrupted ones. In this paper we propose a multiclass boostingframework to ensure adversarial robustness. Boosting algorithms are generallywell-suited for adversarial scenarios, as they were classically designed tosatisfy a minimax guarantee. We provide a theoretical foundation for thismethodology and describe conditions under which robustness can be achievedgiven a weak training oracle. We show empirically that adversarially-robustmulticlass boosting not only outperforms the state-of-the-art methods, it doesso at a fraction of the training time.

A Multiclass Boosting Framework for Achieving Fast and Provable Adversarial Robustness