The Third International Verification of Neural Networks Competition (VNN-COMP 2022): Summary and Results

TL;DR

The paper reports on the third edition of VNN-COMP, an annual competition that benchmarks neural network verification tools, highlighting standardized formats, evaluation procedures, and key results from 11 participating teams.

Contribution

It introduces standardized formats and evaluation procedures for neural network verification, and provides a comprehensive summary of the 2022 competition results.

Findings

11 teams participated with diverse tools

12 benchmarks were evaluated

Insights on tool performance and challenges

Abstract

This report summarizes the 3rd International Verification of Neural Networks Competition (VNN-COMP 2022), held as a part of the 5th Workshop on Formal Methods for ML-Enabled Autonomous Systems (FoMLAS), which was collocated with the 34th International Conference on Computer-Aided Verification (CAV). VNN-COMP is held annually to facilitate the fair and objective comparison of state-of-the-art neural network verification tools, encourage the standardization of tool interfaces, and bring together the neural network verification community. To this end, standardized formats for networks (ONNX) and specification (VNN-LIB) were defined, tools were evaluated on equal-cost hardware (using an automatic evaluation pipeline based on AWS instances), and tool parameters were chosen by the participants before the final test sets were made public. In the 2022 iteration, 11 teams participated on a diverse set of 12 scored benchmarks. This report summarizes the rules, benchmarks, participating tools, results, and lessons learned from this iteration of this competition.