TL;DR
This paper introduces BICCOS, a scalable cutting-plane method leveraging neural network structure for improved verification, outperforming prior approaches especially on large networks.
Contribution
BICCOS is a novel neural network verification method that generates efficient, structure-aware cuts, significantly enhancing scalability and verification success.
Findings
BICCOS generates hundreds of useful cuts during verification.
BICCOS increases the number of verifiable instances on large networks.
BICCOS is part of the VNN-COMP 2024 winner.
Abstract
Recently, cutting-plane methods such as GCP-CROWN have been explored to enhance neural network verifiers and made significant advances. However, GCP-CROWN currently relies on generic cutting planes (cuts) generated from external mixed integer programming (MIP) solvers. Due to the poor scalability of MIP solvers, large neural networks cannot benefit from these cutting planes. In this paper, we exploit the structure of the neural network verification problem to generate efficient and scalable cutting planes specific for this problem setting. We propose a novel approach, Branch-and-bound Inferred Cuts with COnstraint Strengthening (BICCOS), which leverages the logical relationships of neurons within verified subproblems in the branch-and-bound search tree, and we introduce cuts that preclude these relationships in other subproblems. We develop a mechanism that assigns influence scores to…
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