mlsanityData to 7 Oct 2026

ModelVerification.jl: a Comprehensive Toolbox for Formally Verifying Deep Neural Networks

arXiv 2407.01639 ↗First posted 30 Jun 2024cs.LG cs.SEcited by 7

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Abstract (arXiv)

Deep Neural Networks (DNN) are crucial in approximating nonlinear functions across diverse applications, ranging from image classification to control. Verifying specific input-output properties can be a highly challenging task due to the lack of a single, self-contained framework that allows a complete range of verification types. To this end, we present textttModelVerification.jl (MV), the first comprehensive, cutting-edge toolbox that contains a suite of state-of-the-art methods for verifying different types of DNNs and safety specifications. This versatile toolbox is designed to empower developers and machine learning practitioners with robust tools for verifying and ensuring the trustworthiness of their DNN models.

Key facts. Cited by 7 papers in the map (data through 7 Oct 2026). Builds on 30 of its 53 references that are in the map. First posted on arXiv 30 Jun 2024.

Builds on

30 of 53 references.

References · 30

  1. 2015Deep Residual Learning for Image Recognitioncited by 51,809
  2. 2013Intriguing properties of neural networkscited by 5,114
  3. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 739
  4. 2018Efficient Neural Network Robustness Certification with General Activation Functionscited by 439
  5. 2015DeepDriving: Learning Affordance for Direct Perception in Autonomous Drivingcited by 229
  6. 2019Algorithms for Verifying Deep Neural Networkscited by 221
  7. 2020Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifierscited by 183
  8. 2017An approach to reachability analysis for feed-forward ReLU neural networkscited by 126
  9. 2022General Cutting Planes for Bound-Propagation-Based Neural Network Verificationcited by 102
  10. 2022Complete Verification via Multi-Neuron Relaxation Guided Branch-and-Boundcited by 92
  11. 2016Measuring Neural Net Robustness with Constraintscited by 90
  12. 2020Verification of Deep Convolutional Neural Networks Using ImageStarscited by 75
18 more
  1. 2020Enabling certification of verification-agnostic networks via memory-efficient semidefinite programmingcited by 65
  2. 2023First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)cited by 58
  3. 2022The Third International Verification of Neural Networks Competition (VNN-COMP 2022): Summary and Resultscited by 39
  4. 2022Verifying Learning-Based Robotic Navigation Systemscited by 33
  5. 2022Safe Control Under Input Limits with Neural Control Barrier Functionscited by 27
  6. 2021Verification of Neural-Network Control Systems by Integrating Taylor Models and Zonotopescited by 26
  7. 2017Reachable Set Computation and Safety Verification for Neural Networks with ReLU Activationscited by 22
  8. 2021Safe Control with Neural Network Dynamic Modelscited by 21
  9. 2021Neural Network Repair with Reachability Analysiscited by 19
  10. 2023The #DNN-Verification Problem: Counting Unsafe Inputs for Deep Neural Networkscited by 19
  11. 2023Enumerating Safe Regions in Deep Neural Networks with Provable Probabilistic Guaranteescited by 19
  12. 2019Safe Control Algorithms Using Energy Functions: A Unified Framework, Benchmark, and New Directionscited by 17
  13. 2021Towards Hierarchical Task Decomposition using Deep Reinforcement Learning for Pick and Place Subtaskscited by 13
  14. 2022Reachability Analysis of a General Class of Neural Ordinary Differential Equationscited by 13
  15. 2018Reachability Analysis and Safety Verification for Neural Network Control Systemscited by 12
  16. 2021Curriculum Learning for Safe Mapless Navigationcited by 11
  17. 2020From Design Draft to Real Attire: Unaligned Fashion Image Translationcited by 5
  18. 2024Meta-Control: Automatic Model-based Control Synthesis for Heterogeneous Robot Skillscited by 2

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 3; 2026: 4.

7 papers.

  1. 2025Probabilistically Tightened Linear Relaxation-based Perturbation Analysis for Neural Network Verificationcited by 2
  2. 2025Formal Verification of Variational Quantum Circuitscited by 1
  3. 2026Provably Safe, Yet Scalable Reinforcement Learningcited by 1
  4. 2025Enhancing Certifiable Semantic Robustness via Robust Pruning of Deep Neural Networkscited by 0
  5. 2026From Decoupled to Coupled: Robustness Verification for Learning-based Keypoint Detection with Joint Specificationscited by 0
  6. 2026Probabilistic Verification of Recurrent Neural Networks for Single and Multi-Agent Reinforcement Learningcited by 0
  7. 2026A Survey on the Verification of Reinforcement Learning Policiescited by 0