Published in Neural Information Processing Systems (NeurIPS), 2021

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tl;dr: Overconfident inputs nearby the data may cause adversarial vulnerability in randomized smoothing, and regularizing them toward the uniform confidence improves robustness.

  • Also appeared at ICML AdvML Workshop 2021
Additional information


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.


  title={Smooth{Mix}: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness},
  author={Jongheon Jeong and Sejun Park and Minkyu Kim and Heung-Chang Lee and Doguk Kim and Jinwoo Shin},
  booktitle={Advances in Neural Information Processing Systems},