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The Fourth International Verification of Neural Networks Competition (VNN-COMP 2023): Summary and Results
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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.
Forward citations
Cited by 4 Pith papers
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Learning Lookahead Lemmas for Neural Network Verification
A lookahead-based inprocessing framework derives implication lemmas over ReLU phases and vivifies boolean cuts, solving up to 34% more unsatisfiable instances in Marabou and α-β-CROWN.
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Mining Verdict Boundaries for Neural Network Verification
BMiner speeds up Branch-and-Bound neural network verification by using exponential and gradient-guided search to skip subproblems on the way to each path's verdict boundary, cutting average verification time by 17–30%.
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A Survey on the Verification of Reinforcement Learning Policies
A unifying taxonomy of post-training RL-policy verification methods along formal/probabilistic, step-wise/multi-step, and guarantee-strength axes, plus benchmark-based tool-selection guidance.
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Position: Certified Robustness Does Not (Yet) Imply Model Security
A certified robustness radius says nothing about whether a sample is clean or correctly predicted, so certification does not yet imply model security.
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