First three years of the international verification of neural networks competition (VNN-COMP)

Author:

Brix Christopher,Müller Mark Niklas,Bak Stanley,Johnson Taylor T.,Liu Changliu

Abstract

AbstractThis paper presents a summary and meta-analysis of the first three iterations of the annual International Verification of Neural Networks Competition (VNN-COMP), held in 2020, 2021, and 2022. In the VNN-COMP, participants submit software tools that analyze whether given neural networks satisfy specifications describing their input-output behavior. These neural networks and specifications cover a variety of problem classes and tasks, corresponding to safety and robustness properties in image classification, neural control, reinforcement learning, and autonomous systems. We summarize the key processes, rules, and results, present trends observed over the last three years, and provide an outlook into possible future developments.

Funder

RWTH Aachen University

Publisher

Springer Science and Business Media LLC

Subject

Information Systems,Software

Reference69 articles.

1. Simple Adversarial Generator. https://github.com/stanleybak/simple_adversarial_generator. Accessed: 2022-09-13

2. VNN-COMP2020 report. https://www.overleaf.com/project/5f0c85e8d15dc10001749fa9. Accessed: 2022-08-28

3. Bai, J., Lu, F., Zhang, K., et al.: Onnx: open neural network exchange (2019). https://github.com/onnx/onnx

4. Bak, S.: Execution-guided overapproximation (ego) for improving scalability of neural network verification (2020)

5. Bak, S., Liu, C., Johnson, T.: The second international verification of neural networks competition (VNN-COMP 2021): summary and results (2021). https://doi.org/10.48550/ARXIV.2109.00498

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