On Norm-Agnostic Robustness of Adversarial Training
CoRR(2019)
Abstract
Adversarial examples are carefully perturbed in-puts for fooling machine learning models. A well-acknowledged defense method against such examples is adversarial training, where adversarial examples are injected into training data to increase robustness. In this paper, we propose a new attack to unveil an undesired property of the state-of-the-art adversarial training, that is it fails to obtain robustness against perturbations in ℓ_2 and ℓ_∞ norms simultaneously. We discuss a possible solution to this issue and its limitations as well.
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Key words
adversarial training,robustness,norm-agnostic
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