  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.
