Provable Robustness of ReLU Networks via Maximization of Linear Regions
Abstract
It has been shown that neural network classifiers are not robust. This raises concerns about their usage in safety-critical systems. We propose in this paper a regularization scheme for ReLU networks which provably improves the robustness of the classifier by maximizing the linear regions of the classifier as well as the distance to the decision boundary. Using our regularization we can even find the minimal adversarial perturbation for a certain fraction of test points for large networks. In the experiments we show that our approach improves upon pure adversarial training both in terms of lower and upper bounds on the robustness and is comparable or better than the state of the art in terms of test error and robustness.
Cite
Text
Croce et al. "Provable Robustness of ReLU Networks via Maximization of Linear Regions." Artificial Intelligence and Statistics, 2019.Markdown
[Croce et al. "Provable Robustness of ReLU Networks via Maximization of Linear Regions." Artificial Intelligence and Statistics, 2019.](https://mlanthology.org/aistats/2019/croce2019aistats-provable/)BibTeX
@inproceedings{croce2019aistats-provable,
title = {{Provable Robustness of ReLU Networks via Maximization of Linear Regions}},
author = {Croce, Francesco and Andriushchenko, Maksym and Hein, Matthias},
booktitle = {Artificial Intelligence and Statistics},
year = {2019},
pages = {2057-2066},
volume = {89},
url = {https://mlanthology.org/aistats/2019/croce2019aistats-provable/}
}