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