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Randomized Smoothing (RS) is a promising method for obtaining robustnesscertificates by evaluating a base model under noise. In this work, we: (i)theoretically motivate why ensembles are a particularly suitable choice as basemodels for RS, and (ii) empirically confirm this choice, obtainingstate-of-the-art results in multiple settings. The key insight of our work isthat the reduced variance of ensembles over the perturbations introduced in RSleads to significantly more consistent classifications for a given input. This,in turn, leads to substantially increased certifiable radii for samples closeto the decision boundary. Additionally, we introduce key optimizations whichenable an up to 55-fold decrease in sample complexity of RS for predeterminedradii, thus drastically reducing its computational overhead. Experimentally, weshow that ensembles of only 3 to 10 classifiers consistently improve on theirstrongest constituting model with respect to their average certified radius(ACR) by 5% to 21% on both CIFAR10 and ImageNet, achieving a newstate-of-the-art ACR of 0.86 and 1.11, respectively. We release all code andmodels required to reproduce our results athttps://github.com/eth-sri/smoothing-ensembles.

Boosting Randomized Smoothing with Variance Reduced Classifiers