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arXiv · 2208.07289

A Tool for Neural Network Global Robustness Certification and Training

Zhilu Wang·Yixuan Wang·Feisi Fu·Ruochen Jiao·Chao Huang·Wenchao Li·Qi Zhu
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Abstract

With the increment of interest in leveraging machine learning technology in safety-critical systems, the robustness of neural networks under external disturbance receives more and more concerns. Global robustness is a robustness property defined on the entire input domain. And a certified globally robust network can ensure its robustness on any possible network input. However, the state-of-the-art global robustness certification algorithm can only certify networks with at most several thousand neurons. In this paper, we propose the GPU-supported global robustness certification framework GROCET, which is more efficient than the previous optimization-based certification approach. Moreover, GROCET provides differentiable global robustness, which is leveraged in the training of globally robust neural networks.

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Zhilu Wang, Yixuan Wang, Feisi Fu, Ruochen Jiao, Chao Huang, Wenchao Li, Qi Zhu. 2022-08-15. A Tool for Neural Network Global Robustness Certification and Training. https://arxiv.org/abs/2208.07289

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