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Consistency Regularization for Adversarial Robustness
Jihoon Tack · Sihyun Yu · Jongheon Jeong · Minseon Kim · Sung Ju Hwang · Jinwoo Shin

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in A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning »

Adversarial training (AT) is currently one of the most successful methods to obtain the adversarial robustness of deep neural networks. However, the phenomenon of robust overfitting, i.e., the robustness starts to decrease significantly during AT, has been problematic, not only making practitioners consider a bag of tricks for a successful training, e.g., early stopping, but also incurring a significant generalization gap in the robustness. In this paper, we propose an effective regularization technique that prevents robust overfitting by optimizing an auxiliary 'consistency' regularization loss during AT. Specifically, it forces the predictive distributions after attacking from two different augmentations of the same instance to be similar with each other. Our experimental results demonstrate that such a simple regularization technique brings significant improvements in the test robust accuracy of a wide range of AT methods. More remarkably, we also show that our method could significantly help the model to generalize its robustness against unseen adversaries, e.g., other types or larger perturbations compared to those used during training.

Author Information

Jihoon Tack (KAIST)
Sihyun Yu (Korea Advanced Institute of Science and Technology)
Jongheon Jeong (KAIST)
Minseon Kim (Korea Advanced Institute of Science and Technology)
Sung Ju Hwang (UNIST)
Jinwoo Shin (KAIST)

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