Boosting Randomized Smoothing with Variance Reduced Classifiers

Understand

Randomized Smoothing (RS) is a promising method for obtaining robustness certificates by evaluating a base model under noise.

• In this work, we: (i) theoretically motivate why ensembles are a particularly suitable choice as base models for RS, and (ii) empirically confirm this choice, obtaining state-of-the-art results in multiple settings.
• The key insight of our work is that the reduced variance of ensembles over the perturbations introduced in RS leads to significantly more consistent classifications for a given input.
• This, in turn, leads to substantially increased certifiable radii for samples close to the decision boundary.