ICLR 2019poster241 citations
Boosting Robustness Certification of Neural Networks
Gagandeep Singh, Timon Gehr, Markus Püschel, Martin Vechev
Abstract
We present a novel approach for the certification of neural networks against adversarial perturbations which combines scalable overapproximation methods with precise (mixed integer) linear programming. This results in significantly better precision than state-of-the-art verifiers on challenging feedforward and convolutional neural networks with piecewise linear activation functions.
Robustness certificationAdversarial AttacksAbstract InterpretationMILP SolversVerification of Neural Networks
BibTeX
@inproceedings{
singh2018robustness,
title={Robustness Certification with Refinement},
author={Gagandeep Singh and Timon Gehr and Markus Püschel and Martin Vechev},
booktitle={International Conference on Learning Representations},
year={2019},
url={https://openreview.net/forum?id=HJgeEh09KQ},
}