Compositional Neural Network Verification via Assume-Guarantee Reasoning

Hai Duong, David Shriver, ThanhVu Nguyen, Matthew B. Dwyer

Keywords: DNN Verification, Compositional Verification, Assume-Guarantee Reasoning

TL;DR: We propose an assume-guarantee compositional framework, CoVeNN, to scale verification to large networks. CoVeNN can verify nearly 7 times more problems than state-of-the-art verifiers.

Abstract:

Verifying the behavior of neural networks is necessary if developers
are to confidently deploy them as parts of mission-critical systems.
Toward this end, researchers have been actively developing a range of increasingly sophisticated and scalable neural network verifiers.
However, scaling verification to large networks is challenging, at least in part due to the significant memory requirements of verification algorithms.
In this paper, we propose an assume-guarantee compositional framework, CoVeNN, that is parameterized by an underlying verifier to generate a sequence of verification sub-problems to address this challenge.
We present an iterative refinement-based strategy for computing assumptions
that allow sub-problems to retain sufficient accuracy.
An evaluation using 7 neural networks and a total of 140 property specifications demonstrates that CoVeNN can verify nearly 7 times more problems than state-of-the-art verifiers.
CoVeNN is part of the NeuralSAT verification project: https://github.com/dynaroars/neuralsat.

Primary Area: Deep learning (e.g., architectures, generative models, optimization for deep networks, foundation models, LLMs)

Submission Number: 13250