Publications

Complete list of peer-reviewed publications using or describing NNV.

Core Tool Papers

    1. Manzanas Lopez, S.W. Choi, H.-D. Tran, T.T. Johnson, “NNV 2.0: The Neural Network Verification Tool”, CAV 2023. DOI: 10.1007/978-3-031-37703-7_19

  • H.-D. Tran, X. Yang, D. Manzanas Lopez, P. Musau, L.V. Nguyen, W. Xiang, S. Bak, T.T. Johnson, “NNV: A Tool for Verification of Deep Neural Networks and Learning-Enabled Autonomous CPS”, CAV 2020. DOI: 10.1007/978-3-030-53288-8_1

  • H.-D. Tran, P. Musau, D. Manzanas Lopez, X. Yang, L.V. Nguyen, W. Xiang, T.T. Johnson, “Star-Based Reachability Analysis for Deep Neural Networks”, FM 2019.

NNV 3.0 Feature Papers

    1. Sasaki, D. Manzanas Lopez, P.K. Robinette, T.T. Johnson, “Robustness Verification of Video Classification Neural Networks”, FormaliSE 2025. DOI: 10.1109/FormaliSE66629.2025.00009

    1. Hashemi, S. Sasaki, I. Oguz, M. Ma, T.T. Johnson, “Scaling Data-Driven Probabilistic Robustness Analysis for Semantic Segmentation Neural Networks”, NeurIPS 2025.

  • A.M. Tumlin, D. Manzanas Lopez, P. Robinette, Y. Zhao, T. Derr, T.T. Johnson, “FairNNV: The Neural Network Verification Tool For Certifying Fairness”, ICAIF 2024. DOI: 10.1145/3677052.3698677

  • M.U. Zubair, T.T. Johnson, K. Basu, W. Abbas, “Verification of Neural Network Robustness Against Weight Perturbations Using Star Sets”, IEEE CAI 2025. DOI: 10.1109/CAI64502.2025.00117

    1. Pal, D. Manzanas Lopez, T.T. Johnson, “Robustness Verification of Deep Neural Networks using Star-Based Reachability Analysis with Variable-Length Time Series Input”, FMICS 2023.

  • P.K. Robinette, D. Manzanas Lopez, S. Serbinowska, K. Leach, T.T. Johnson, “Case Study: Neural Network Malware Detection Verification for Feature and Image Datasets”, FormaliSE 2024. DOI: 10.1145/3644033.3644372

Architecture-Specific Papers

  • H.-D. Tran, S. Bak, W. Xiang, T.T. Johnson, “Towards Verification of Large Convolutional Neural Networks Using ImageStars”, CAV 2020.

    1. Bak, H.-D. Tran, K. Hobbs, T.T. Johnson, “Improved Geometric Path Enumeration for Verifying ReLU Neural Networks”, CAV 2020.

  • H.-D. Tran, N. Pal, P. Musau, X. Yang, N. Hamilton, D. Manzanas Lopez, S. Bak, T.T. Johnson, “Robustness Verification of Semantic Segmentation Neural Networks using Relaxed Reachability”, CAV 2021.

  • H.D. Tran, S.W. Choi, X. Yang, T. Yamaguchi, B. Hoxha, D. Prokhorov, “Verification of Recurrent Neural Networks with Star Reachability”, HSCC 2023. DOI: 10.1145/3575870.3587128

    1. Ivashchenko, S.W. Choi, L.V. Nguyen, H.-D. Tran, “Verifying Binary Neural Networks on Continuous Input Space using Star Reachability”, FormaliSE 2023. DOI: 10.1109/FormaliSE58978.2023.00009

    1. Manzanas Lopez, P. Musau, N. Hamilton, T.T. Johnson, “Reachability Analysis of a General Class of Neural Ordinary Differential Equations”, FORMATS 2022.

Application Papers

    1. Manzanas Lopez, T.T. Johnson, S. Bak, H.-D. Tran, K. Hobbs, “Evaluation of Neural Network Verification Methods for Air to Air Collision Avoidance”, AIAA JAT 2022.

  • T.T. Johnson, D. Manzanas Lopez, H.-D. Tran, “Tutorial: Safe, Secure, and Trustworthy AI via Formal Verification of Neural Networks and Autonomous CPS with NNV”, DSN 2024. DOI: 10.1109/DSN-S60304.2024.00027

  • H.-D. Tran, D. Manzanas Lopez, T. Johnson, “Tutorial: Neural Network and Autonomous CPS Formal Verification for Trustworthy AI and Safe Autonomy”, EMSOFT 2023. DOI: 10.1145/3607890.3608454

    1. Manzanas Lopez, S. Sasaki, T.T. Johnson, “NNV: A Star Set Reachability Approach (Competition Contribution)”, SAIV 2025. DOI: 10.1007/978-3-031-99991-8_15

Foundational Papers

  • H.-D. Tran, W. Xiang, T.T. Johnson, “Verification Approaches for Learning-Enabled Autonomous CPS”, IEEE D&T 2020.

  • H.-D. Tran, F. Cei, D. Manzanas Lopez, T.T. Johnson, X. Koutsoukos, “Safety Verification of CPS with Reinforcement Learning Control”, EMSOFT 2019.

  • H.-D. Tran, P. Musau, D. Manzanas Lopez, X. Yang, L.V. Nguyen, W. Xiang, T.T. Johnson, “Parallelizable Reachability Analysis Algorithms for FeedForward Neural Networks”, FormaliSE 2019.

  • L.C. Cordeiro et al., “Neural Network Verification is a Programming Language Challenge”, ESOP 2025. DOI: 10.1007/978-3-031-91118-7_9

    1. Xiang, H.-D. Tran, T.T. Johnson, “Output Reachable Set Estimation and Verification for Multi-Layer Neural Networks”, IEEE TNNLS 2018.

VNN-COMP Reports

    1. Brix, S. Bak, T.T. Johnson, H. Wu, “The Fifth International Verification of Neural Networks Competition (VNN-COMP 2024)”, arXiv:2412.19985, 2024.

    1. Brix, S. Bak, C. Liu, T.T. Johnson, “The Fourth International Verification of Neural Networks Competition (VNN-COMP 2023)”, arXiv:2312.16760, 2023.

  • M.N. Muller, C. Brix, S. Bak, C. Liu, T.T. Johnson, “The Third International Verification of Neural Networks Competition (VNN-COMP 2022)”, arXiv:2212.10376, 2022.

    1. Bak, C. Liu, T.T. Johnson, “The Second International Verification of Neural Networks Competition (VNN-COMP 2021)”, arXiv:2109.00498, 2021.

    1. Brix, M.N. Muller, S. Bak, T.T. Johnson, C. Liu, “First Three Years of the International Verification of Neural Networks Competition (VNN-COMP)”, STTT 2023. DOI: 10.1007/s10009-023-00703-4