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The Fifth International Verification of Neural Networks Competition (VNN-COMP 2024): Summary and Results
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This report summarizes the 5th International Verification of Neural Networks Competition (VNN-COMP 2024), held as a part of the 7th International Symposium on AI Verification (SAIV), that was collocated with the 36th 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 2024 iteration, 8 teams participated on a diverse set of 12 regular and 8 extended benchmarks. This report summarizes the rules, benchmarks, participating tools, results, and lessons learned from this iteration of this competition.
Forward citations
Cited by 8 Pith papers
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IoUCert: Robustness Verification for Anchor-based Object Detectors
IoUCert derives exact IoU bounds over anchor-offset boxes via a coordinate transformation and uses them to formally verify single-object SSD, YOLOv2, and YOLOv3 models under brightness, contrast, and motion-blur pertu...
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Of Good Demons and Bad Angels: Guaranteeing Safe Control under Finite Precision
A dL/dGL-based method that verifies infinite-horizon safety of neural network controllers under bounded finite-precision perturbations and synthesizes sound mixed-precision fixed-point implementations.
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Interior-Point Vanishing Problem in Semidefinite Relaxations for Neural Network Verification
Semidefinite relaxation for deep ReLU verification suffers from 'interior-point vanishing' as depth increases, and removing layer-wise bound constraints mitigates it.
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SDP-CROWN: Efficient Bound Propagation for Neural Network Verification with Tightness of Semidefinite Programming
SDP-CROWN's deep-network integration is unsound: the per-layer L2 ball is centered at the linear network's preactivation instead of the actual forward preactivation, allowing invalid robustness certificates.
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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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Learning to Split: A Reinforcement-Learning-Guided Splitting Heuristic for Neural Network Verification
A DQfD-trained ReLU-splitting policy modestly improves Marabou's average verification time on ACAS Xu, but not the number of iterations as claimed.
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Efficient Certified Reasoning for Binarized Neural Networks
A native BNN-aware solver and proof-checking pipeline certifies 99% of qualitative and 86% of quantitative robustness queries, with 9x and 218x speedups over prior certified baselines.
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SAIL: Sound Abstract Interpreters with LLMs
SAIL synthesizes globally sound abstract transformers for neural-network operators by combining LLM generation with syntactic validation, SMT-based soundness checking, and cost-guided iterative refinement.
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