mlsanityData to 14 Oct 2026

A DPLL(T) Framework for Verifying Deep Neural Networks

arXiv 2307.10266 ↗First posted 17 Jul 2023cs.LG cs.LO cs.SEcited by 24

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Abstract (arXiv)

Deep Neural Networks (DNNs) have emerged as an effective approach to tackling real-world problems. However, like human-written software, DNNs can have bugs and can be attacked. To address this, research has explored a wide-range of algorithmic approaches to verify DNN behavior. In this work, we introduce NeuralSAT, a new verification approach that adapts the widely-used DPLL(T) algorithm used in modern SMT solvers. A key feature of SMT solvers is the use of conflict clause learning and search restart to scale verification. Unlike prior DNN verification approaches, NeuralSAT combines an abstraction-based deductive theory solver with clause learning and an evaluation clearly demonstrates the benefits of the approach on a set of challenging verification benchmarks.

Key facts. Cited by 24 papers in the map (data through 14 Oct 2026). Builds on 38 of its 109 references that are in the map. First posted on arXiv 17 Jul 2023.

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38 of 109 references.

References · 38

  1. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  2. 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
  3. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  4. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  5. 2013Intriguing properties of neural networkscited by 5,155
  6. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
  7. 2018Efficient Neural Network Robustness Certification with General Activation Functionscited by 445
  8. 2018Adversarial Attacks on Neural Networks for Graph Datacited by 397
  9. 2016Safety Verification of Deep Neural Networkscited by 263
  10. 2017Formal Verification of Piece-Wise Linear Feed-Forward Neural Networkscited by 242
  11. 2021Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verificationcited by 228
  12. 2019Algorithms for Verifying Deep Neural Networkscited by 221
26 more
  1. 2020Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifierscited by 187
  2. 2018Formal Security Analysis of Neural Networks using Symbolic Intervalscited by 185
  3. 2018Efficient Formal Safety Analysis of Neural Networkscited by 165
  4. 2019Branch and Bound for Piecewise Linear Neural Network Verificationcited by 138
  5. 2022General Cutting Planes for Bound-Propagation-Based Neural Network Verificationcited by 103
  6. 2022Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Boundcited by 92
  7. 2021PRIMA: General and Precise Neural Network Certification via Scalable Convex Hull Approximationscited by 77
  8. 2022Natural Attack for Pre-trained Models of Codecited by 75
  9. 2021The Second International Verification of Neural Networks Competition (VNN-COMP 2021): Summary and Resultscited by 71
  10. 2020CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray imagescited by 68
  11. 2018Training for Faster Adversarial Robustness Verification via Inducing ReLU Stabilitycited by 67
  12. 2023First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)cited by 58
  13. 2021Improved Branch and Bound for Neural Network Verification via Lagrangian Decompositioncited by 44
  14. 2020Scaling Polyhedral Neural Network Verification on GPUscited by 43
  15. 2022The Third International Verification of Neural Networks Competition (VNN-COMP 2022): Summary and Resultscited by 39
  16. 2020Parallelization Techniques for Verifying Neural Networkscited by 36
  17. 2021A Review of Formal Methods applied to Machine Learningcited by 33
  18. 2017An Analysis of ISO 26262: Using Machine Learning Safely in Automotive Softwarecited by 26
  19. 2017Towards Proving the Adversarial Robustness of Deep Neural Networkscited by 26
  20. 2021DNNV: A Framework for Deep Neural Network Verificationcited by 23
  21. 2022Neural Network Verification with Proof Productioncited by 22
  22. 2022Can pruning improve certified robustness of neural networks?cited by 14
  23. 2022Linearity Grafting: Relaxed Neuron Pruning Helps Certifiable Robustnesscited by 10
  24. 2021Static analysis of ReLU neural networks with tropical polyhedracited by 5
  25. 2021Fast Falsification of Neural Networks using Property Directed Testingcited by 2
  26. 2021FLACK: Counterexample-Guided Fault Localization for Alloy Modelscited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 1; 2025: 9; 2026: 14.

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  1. 2024Scalable Neural Network Verification with Branch-and-bound Inferred Cutting Planescited by 33
  2. 2025Clip-and-Verify: Linear Constraint-Driven Domain Clipping for Accelerating Neural Network Verificationcited by 9
  3. 2025Out of the Shadows: Exploring a Latent Space for Neural Network Verificationcited by 6
  4. 2025Proof Minimization in Neural Network Verificationcited by 4
  5. 2026Exact Certification of Data-Poisoning Attacks Using Mixed-Integer Programmingcited by 4
  6. 2025PICID: Proof-Driven Clause Learning in Neural Network Verificationcited by 3
  7. 2025Probabilistic Robustness Analysis in High Dimensional Space: Application to Semantic Segmentation Networkcited by 3
  8. 2026IoUCert: Robustness Verification for Anchor-based Object Detectorscited by 1
  9. 2026ZonoGPT: Towards An Abstract Domain for Verifying Large GPT Modelscited by 1
  10. 2025A Guide to Failure in Machine Learning: Reliability and Robustness from Foundations to Practicecited by 0
  11. 2025BoxRL-NNV: Boxed Refinement of Latin Hypercube Samples for Neural Network Verificationcited by 0
  12. 2025Efficient Neural Network Verification via Order Leading Exploration of Branch-and-Bound Treescited by 0
12 more
  1. 2025Influence-Guided Concolic Testing of Transformer Robustnesscited by 0
  2. 2026E-Globe: Scalable ε-Global Verification of Neural Networks via Tight Upper Bounds and Pattern-Aware Branchingcited by 0
  3. 2026Lipschitz-Based Robustness Certification Under Floating-Point Executioncited by 0
  4. 2026Stress-Testing Neural Network Verifiers with Provably Robust Instancescited by 0
  5. 2026Repurposing CLIP to Localize at Pixel Levelcited by 0
  6. 2026Trustworthy Machine Learning through the Lens of Combinatorial Optimization: Survey and Research Perspectivescited by 0
  7. 2026A Survey on the Verification of Reinforcement Learning Policiescited by 0
  8. 2026Certified but Private: Scalable Zero-Knowledge Proofs for Neural Network Guaranteescited by 0
  9. 2026NNV3: Expanding Neural Network Verification to New Architectures and Domainscited by 0
  10. 2026Verifying Neural Networks with Reinforcement Learningcited by 0
  11. 2026CLAD: Constrained Abstract Domain for Neural Network Verificationcited by 0
  12. 2026Neural Network Verification for Deep Joint Source-Channel Codingcited by 0