Papers › Consistency Regularization for Certified Robustness of Smoothed Classifiers

Consistency Regularization for Certified Robustness of Smoothed Classifiers

7 Jun 2020NeurIPS 2020 12arXiv:2006.04062archive 2025-07-28

Jongheon Jeong, Jinwoo Shin

A recent technique of randomized smoothing has shown that the worst-case (adversarial) ℓ₂-robustness can be transformed into the average-case Gaussian-robustness by "smoothing" a classifier, i.e., by considering the averaged prediction over Gaussian noise. In this paradigm, one should rethink the notion of adversarial robustness in terms of generalization ability of a classifier under noisy observations. We found that the trade-off between accuracy and certified robustness of smoothed classifiers can be greatly controlled by simply regularizing the prediction consistency over noise. This relationship allows us to design a robust training objective without approximating a non-existing smoothed classifier, e.g., via soft smoothing. Our experiments under various deep neural network architectures and datasets show that the "certified" ℓ₂-robustness can be dramatically improved with the proposed regularization, even achieving better or comparable results to the state-of-the-art approaches with significantly less training costs and hyperparameters.

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