Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding

Wang, Zhilu, Huang, Chao ORCID: 0000-0002-9300-1787 and Zhu, Qi (2022) Efficient Global Robustness Certification of Neural Networks via Interleaving Twin-Network Encoding. In: 2022 Design, Automation & Test in Europe Conference & Exhibition (DATE), 2022-3-14 - 2022-3-23.

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Official URL: http://dx.doi.org/10.23919/date54114.2022.9774719

Abstract

The robustness of deep neural networks has received significant interest recently, especially when being deployed in safety-critical systems, as it is important to analyze how sensitive the model output is under input perturbations. While most previous works focused on the local robustness property around an input sample, the studies of the global robustness property, which bounds the maximum output change under perturbations over the entire input space, are still lacking. In this work, we formulate the global robustness certification for neural networks with ReLU activation functions as a mixed-integer linear programming (MILP) problem, and present an efficient approach to address it. Our approach includes a novel interleaving twin-network encoding scheme, where two copies of the neural network are encoded side-by-side with extra interleaving dependencies added between them, and an over-approximation algorithm leveraging relaxation and refinement techniques to reduce complexity. Experiments demonstrate the timing efficiency of our work when compared with previous global robustness certification methods and the tightness of our over-approximation. A case study of closed-loop control safety verification is conducted, and demonstrates the importance and practicality of our approach for certifying the global robustness of neural networks in safety-critical systems.

Item Type:	Conference or Workshop Item (Unspecified)
Uncontrolled Keywords:	1.1 Normal biological development and functioning, 1 Underpinning research
Divisions:	Faculty of Science and Engineering > School of Electrical Engineering, Electronics and Computer Science
Depositing User:	Symplectic Admin
Date Deposited:	09 Sep 2022 08:15
Last Modified:	15 Mar 2024 17:58
DOI:	10.23919/date54114.2022.9774719
Open Access URL:	https://arxiv.org/pdf/2203.14141.pdf
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3163977