mlsanityData to 14 Oct 2026

DeepCDCL: An CDCL-based Neural Network Verification Framework

arXiv 2403.07956 ↗First posted 12 Mar 2024cs.LG cs.AIcited by 6

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Abstract (arXiv)

Neural networks in safety-critical applications face increasing safety and security concerns due to their susceptibility to little disturbance. In this paper, we propose DeepCDCL, a novel neural network verification framework based on the Conflict-Driven Clause Learning (CDCL) algorithm. We introduce an asynchronous clause learning and management structure, reducing redundant time consumption compared to the direct application of the CDCL framework. Furthermore, we also provide a detailed evaluation of the performance of our approach on the ACAS Xu and MNIST datasets, showing that a significant speed-up is achieved in most cases.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 23 of its 47 references that are in the map. First posted on arXiv 12 Mar 2024.

Builds on

23 of 47 references.

References · 23

  1. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  2. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
  3. 2016Safety Verification of Deep Neural Networkscited by 263
  4. 2018Towards Fast Computation of Certified Robustness for ReLU Networkscited by 255
  5. 2017Formal Verification of Piece-Wise Linear Feed-Forward Neural Networkscited by 242
  6. 2021Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verificationcited by 228
  7. 2019Towards Stable and Efficient Training of Verifiably Robust Neural Networkscited by 205
  8. 2020Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifierscited by 187
  9. 2018Formal Security Analysis of Neural Networks using Symbolic Intervalscited by 185
  10. 2018Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approachcited by 182
  11. 2018Efficient Formal Safety Analysis of Neural Networkscited by 165
  12. 2020NNV: The Neural Network Verification Tool for Deep Neural Networks and Learning-Enabled Cyber-Physical Systemscited by 128
11 more
  1. 2022General Cutting Planes for Bound-Propagation-Based Neural Network Verificationcited by 103
  2. 2022Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Boundcited by 92
  3. 2016Measuring Neural Net Robustness with Constraintscited by 90
  4. 2021PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximationscited by 77
  5. 2018Deep Neural Network Compression for Aircraft Collision Avoidance Systemscited by 58
  6. 2018CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networkscited by 50
  7. 2019Optimization and Abstraction: A Synergistic Approach for Analyzing Neural Network Robustnesscited by 40
  8. 2023The Fourth International Verification of Neural Networks Competition (VNN-COMP 2023): Summary and Resultscited by 38
  9. 2020DeepAbstract: Neural Network Abstraction for Accelerating Verificationcited by 31
  10. 2020Improving Neural Network Verification through Spurious Region Guided Refinementcited by 31
  11. 2019Analyzing Deep Neural Networks with Symbolic Propagation: Towards Higher Precision and Faster Verificationcited by 28

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 4; 2026: 1.

6 papers.

  1. 2024Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planescited by 33
  2. 2025Abstraction-Based Proof Production in Formal Verification of Neural Networkscited by 5
  3. 2025Proof Minimization in Neural Network Verificationcited by 4
  4. 2025PICID: Proof-Driven Clause Learning in Neural Network Verificationcited by 3
  5. 2025MUC-G4: Minimal Unsat Core-Guided Incremental Verification for Deep Neural Network Compressioncited by 0
  6. 2026E-Globe: Scalable ε-Global Verification of Neural Networks via Tight Upper Bounds and Pattern-Aware Branchingcited by 0