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Adversarial Robustness against Multiple and Single lₚ-Threat Models via Quick Fine-Tuning of Robust Classifiers

26 May 2021arXiv:2105.12508archive 2025-07-28

Francesco Croce, Matthias Hein

A major drawback of adversarially robust models, in particular for large scale datasets like ImageNet, is the extremely long training time compared to standard ones. Moreover, models should be robust not only to one lₚ-threat model but ideally to all of them. In this paper we propose Extreme norm Adversarial Training (E-AT) for multiple-norm robustness which is based on geometric properties of lₚ-balls. E-AT costs up to three times less than other adversarial training methods for multiple-norm robustness. Using E-AT we show that for ImageNet a single epoch and for CIFAR-10 three epochs are sufficient to turn any lₚ-robust model into a multiple-norm robust model. In this way we get the first multiple-norm robust model for ImageNet and boost the state-of-the-art for multiple-norm robustness to more than 51% on CIFAR-10. Finally, we study the general transfer via fine-tuning of adversarial robustness between different individual lₚ-threat models and improve the previous SOTA l₁-robustness on both CIFAR-10 and ImageNet. Extensive experiments show that our scheme works across datasets and architectures including vision transformers.

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