This report summarizes the 4th International Verification of Neural Networks
Competition (VNN-COMP 2023), held as a part of the 6th Workshop on Formal
Methods for ML-Enabled Autonomous Systems (FoMLAS), that was collocated with
the 35th 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 2023 iteration, 7 teams participated on a diverse set of 10
scored and 4 unscored benchmarks. This report summarizes the rules, benchmarks,
participating tools, results, and lessons learned from this iteration of this
competition.

该报告总结了第四届国际神经网络验证竞赛（VNN-COMP 2023），作为第六届机器学习驱动自主系统形式化方法研讨会（FoMLAS）的一部分，在第 35 届计算机辅助验证国际会议（CAV）上举行。VNN-COMP 是一年一度的活动，旨在促进最先进的神经网络验证工具的公平客观比较，鼓励工具接口的标准化，并汇集神经网络验证社区。本次竞赛中，制定了网络（ONNX）和规格（VNN-LIB）的标准化格式，使用基于 AWS 实例的自动评估流水线在等成本硬件上评估工具，并由参赛者选择工具参数，然后公开最终的测试集。在 2023 年的竞赛中，共有 7 个团队参与了 10 个有分数和 4 个无分数的多样化基准测试。本报告总结了本次竞赛的规则、基准测试、参赛工具、结果和经验教训。