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Machine learning models are vulnerable to adversarial attacks. One approachto addressing this vulnerability is certification, which focuses on models thatare guaranteed to be robust for a given perturbation size. A drawback of recentcertified models is that they are stochastic: they require multiplecomputationally expensive model evaluations with random noise added to a giveninput. In our work, we present a deterministic certification approach whichresults in a certifiably robust model. This approach is based on an equivalencebetween training with a particular regularized loss, and the expected values ofGaussian averages. We achieve certified models on ImageNet-1k by retraining amodel with this loss for one epoch without the use of label information.

Adversarial Boot Camp: label free certified robustness in one epoch