## SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness

### Jongheon Jeong · Sejun Park · Minkyu Kim · Heung-Chang Lee · Do-Guk Kim · Jinwoo Shin

Keywords: [ Adversarial Robustness and Security ] [ Deep Learning ] [ Machine Learning ] [ Robustness ]

[ Abstract ]
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Tue 7 Dec 8:30 a.m. PST — 10 a.m. PST

Abstract: Randomized smoothing is currently a state-of-the-art method to construct a certifiably robust classifier from neural networks against $\ell_2$-adversarial perturbations. Under the paradigm, the robustness of a classifier is aligned with the prediction confidence, i.e., the higher confidence from a smoothed classifier implies the better robustness. This motivates us to rethink the fundamental trade-off between accuracy and robustness in terms of calibrating confidences of a smoothed classifier. In this paper, we propose a simple training scheme, coined SmoothMix, to control the robustness of smoothed classifiers via self-mixup: it trains on convex combinations of samples along the direction of adversarial perturbation for each input. The proposed procedure effectively identifies over-confident, near off-class samples as a cause of limited robustness in case of smoothed classifiers, and offers an intuitive way to adaptively set a new decision boundary between these samples for better robustness. Our experimental results demonstrate that the proposed method can significantly improve the certified $\ell_2$-robustness of smoothed classifiers compared to existing state-of-the-art robust training methods.

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