ICLR 2020poster6 citations

Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$

Francesco Croce, Matthias Hein

Abstract

In recent years several adversarial attacks and defenses have been proposed. Often seemingly robust models turn out to be non-robust when more sophisticated attacks are used. One way out of this dilemma are provable robustness guarantees. While provably robust models for specific $l_p$-perturbation models have been developed, we show that they do not come with any guarantee against other $l_q$-perturbations. We propose a new regularization scheme, MMR-Universal, for ReLU networks which enforces robustness wrt $l_1$- \textit{and} $l_\infty$-perturbations and show how that leads to the first provably robust models wrt any $l_p$-norm for $p\geq 1$.

adversarial robustnessprovable guarantees
BibTeX
@inproceedings{
Croce2020Provable,
title={Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$},
author={Francesco Croce and Matthias Hein},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=rklk_ySYPB}
}
Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$ · ICLR 2020