# Adversarial Boot Camp: label free certified robustness in one epoch

Source: [Adversarial Boot Camp: label free certified robustness in one epoch](<https://www.sota2.com/research/paper/68fa686c1e209131cea1c08a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T22:37:00.137Z

Authors: Ryan Campbell, Chris Finlay, Adam M Oberman

Published: 2020-10-05T13:47:45.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2010.02508>)

[PDF](<https://arxiv.org/pdf/2010.02508.pdf>)