Matlab Toolbox for Neural Network Verification

This toolbox implements reachability methods for analyzing neural networks and control systems with neural network controllers in the area of autonomous cyber-physical systems (CPS).

This toolbox makes use of the neural network model transformation tool (nnmt) and for closed-loop systems analysis, the hybrid systems model transformation and translation tool (HyST), and the COntinuous Reachability Analyzer (CORA).

NNV can be executed online without installing Matlab or other dependencies through CodeOcean via the following CodeOcean capsules:

Latest

• CAV 2023 Tool Paper: https://doi.org/10.24433/CO.0803700.v1

Previous

• CAV 2020 ImageStar paper version: https://doi.org/10.24433/CO.3351375.v1
• CAV 2020 Tool paper version: https://doi.org/10.24433/CO.0221760.v1
• Earliest version: https://doi.org/10.24433/CO.1314285.v1

1. Install MATLAB (2022b or newer) with at least the following toolboxes:

   • Computer Vision
   • Control Systems
   • Deep Learning
   • Image Processing
   • Optimization
   • Parallel Computing
   • Statistics and Machine Learning
   • Symbolic Math
   • System Identification

2. Install the following support package Deep Learning Toolbox Converter for ONNX Model Format

   Note: Support packages can be installed in MATLAB's HOME tab > Add-Ons > Get Add-ons, search for the support package using the Add-on Explorer and click on the Install button.
   

3. Clone or download the NNV toolbox from (https://github.com/verivital/nnv)

   Note: to operate correctly, nnv depends on other tools (CORA, NNMT, HyST, onnx2nnv), which are included as git submodules. As such, you must clone recursively, e.g., with the following:

   git clone --recursive https://github.com/verivital/nnv.git
   

4. Open MATLAB, then go to the directory where NNV exists on your machine, then run the install.m script located at /nnv/

   Note: if you restart Matlab, rerun either install.m or startup_nnv.m, which will add the necessary dependencies to the path; you alternatively can run savepath after installation to avoid this step after restarting Matlab, but this may require administrative privileges

5. Optional installation packages

   a. To run verification for convolutional neural networks (CNNs) on VGG16/VGG19, additional support packages must be installed:

   • VGG16

   • VGG19

   b) To run MATLAB's neural network verification comparison, an additional support package is needed (used in CAV'2023 submission):

   • Deep Learning Toolbox Verification Library

   c) To load models from other deep learning frameworks, please install the additional support packages:

   • TensorFlow and Keras: Deep Learning Toolbox Converter for TensorFlow Models
   • PyTorch: Deep Learning Toolbox Converter for PyTorch Models

Open MATLAB, then go to the /code/nnv/ folder and execute the uninstall.m script.

To get started with NNV, let's take a look at a tutorial containing examples demonstrating:

NN

• Robustness verification on the MNIST dataset.
  • Robustness verification of whole dataset using a model with fully-connected and ReLU layers.
  • Robustness verification of a single image using a model with fully-connected and ReLU layers.
  • Robustness verification of a single image using a model with Convolutional, Pooling, Batch Normalization, ReLU, and fully-connected layers
• Robustness verification on the GTSRB dataset.
  • Includes training and verification scripts as well.
• Comparison of exact (sound and complete) and approximate (sound and incomplete) methods using Star sets exact vs approx

NNCS

• Reachability analysis of an inverted pendulum.
• Safety verification example of an Adaptive Cruise Control (ACC) system.
  • Training. This requires installing Simulink.
  • Safety Verification.
• Safety verification of an Automated Emergency Braking System (AEBS)
  • This system contains several neural networks, so a custom control loop is included in the verification script.

In addition to the examples from the tutorial, there are more examples in the 'code/nnv/examples/' folder, including:

Semantic Segmentation

• Robustness analysis of semantic segmentation NNs

Recurrent Neural Networks

• Robustness analysis of RNNs

Neural Ordinary Differential Equations

• Reachability analysis of neural ODEs

And more other NN and NNCS examples.

To run all the tests, one can run the following command from 'code/nnv/tests/' folder:

runtests(pwd, 'IncludeSubfolders', true);

All the code for the publication using NNV, including competitions that NNV has participated in (e.g. VNNCOMP and ARCH-COMP) can be found in the examples/Submissions folder. For a selected subset of publications, tags were created. To reproduce those results, please download NNV using the corresponding tag or use the corresponding CodeOcean capsules.

• Hoang-Dung Tran
• Diego Manzanas Lopez
• Neelanjana Pal
• Weiming Xiang
• Stanley Bak
• Patrick Musau
• Xiaodong Yang
• Luan Viet Nguyen
• Taylor T. Johnson

The methods implemented in NNV are based upon or used in the following papers:

• Diego Manzanas Lopez, Sung Woo Choi, Hoang-Dung Tran, Taylor T. Johnson, "NNV 2.0: The Neural Network Verification Tool". In: Enea, C., Lal, A. (eds) Computer Aided Verification. CAV 2023. Lecture Notes in Computer Science, vol 13965. Springer, Cham. [https://doi.org/10.1007/978-3-031-37703-7_19]

• Neelanjana Pal, Diego Manzanas Lopez, Taylor T Johnson, "Robustness Verification of Deep Neural Networks using Star-Based Reachability Analysis with Variable-Length Time Series Input", to be presented at FMICS 2023. [https://arxiv.org/pdf/2307.13907.pdf]

• Mykhailo Ivashchenko, Sung Woo Choi, Luan Viet Nguyen, Hoang-Dung Tran, "Verifying Binary Neural Networks on Continuous Input Space using Star Reachability," 2023 IEEE/ACM 11th International Conference on Formal Methods in Software Engineering (FormaliSE), Melbourne, Australia, 2023, pp. 7-17, [https://doi.org/10.1109/FormaliSE58978.2023.00009]

• Hoang Dung Tran, Sung Woo Choi, Xiaodong Yang, Tomoya Yamaguchi, Bardh Hoxha, and Danil Prokhorov. "Verification of Recurrent Neural Networks with Star Reachability". In Proceedings of the 26th ACM International Conference on Hybrid Systems: Computation and Control (HSCC '23). Association for Computing Machinery, New York, NY, USA, Article 6, 1–13. [https://doi.org/10.1145/3575870.3587128]

• Diego Manzanas Lopez, Taylor T. Johnson, Stanley Bak, Hoang-Dung Tran, Kerianne Hobbs, "Evaluation of Neural Network Verification Methods for Air to Air Collision Avoidance", In AIAA Journal of Air Transportation (JAT), 2022 [http://www.taylortjohnson.com/research/lopez2022jat.pdf]

• Diego Manzanas Lopez, Patrick Musau, Nathaniel Hamilton, Taylor T. Johnson, "Reachability Analysis of a General Class of Neural Ordinary Differential Equations", In 20th International Conference on Formal Modeling and Analysis of Timed Systems (FORMATS), 2022 [http://www.taylortjohnson.com/research/lopez2022formats.pdf]

• Hoang-Dung Tran, Neelanjana Pal, Patrick Musau, Xiaodong Yang, Nathaniel P. Hamilton, Diego Manzanas Lopez, Stanley Bak, Taylor T. Johnson, "Robustness Verification of Semantic Segmentation Neural Networks using Relaxed Reachability", In 33rd International Conference on Computer-Aided Verification (CAV), Springer, 2021. [http://www.taylortjohnson.com/research/tran2021cav.pdf]

• Hoang-Dung Tran, Patrick Musau, Diego Manzanas Lopez, Xiaodong Yang, Luan Viet Nguyen, Weiming Xiang, Taylor T.Johnson, "NNV: A Tool for Verification of Deep Neural Networks and Learning-Enabled Autonomous Cyber-Physical Systems", 32nd International Conference on Computer-Aided Verification (CAV), 2020. [http://taylortjohnson.com/research/tran2020cav_tool.pdf]

• Hoang-Dung Tran, Stanley Bak, Weiming Xiang, Taylor T. Johnson, "Towards Verification of Large Convolutional Neural Networks Using ImageStars", 32nd International Conference on Computer-Aided Verification (CAV), 2020. [http://taylortjohnson.com/research/tran2020cav.pdf]

• Stanley Bak, Hoang-Dung Tran, Kerianne Hobbs, Taylor T. Johnson, "Improved Geometric Path Enumeration for Verifying ReLU Neural Networks", In 32nd International Conference on Computer-Aided Verification (CAV), 2020. [http://www.taylortjohnson.com/research/bak2020cav.pdf]

• Hoang-Dung Tran, Weiming Xiang, Taylor T. Johnson, "Verification Approaches for Learning-Enabled Autonomous Cyber-Physical Systems", The IEEE Design & Test 2020. [http://www.taylortjohnson.com/research/tran2020dandt.pdf]

• Hoang-Dung Tran, Patrick Musau, Diego Manzanas Lopez, Xiaodong Yang, Luan Viet Nguyen, Weiming Xiang, Taylor T.Johnson, "Star-Based Reachability Analysis for Deep Neural Networks", The 23rd International Symposium on Formal Methods (FM), Porto, Portugal, 2019, Acceptance Rate 30%. . [http://taylortjohnson.com/research/tran2019fm.pdf]

• Hoang-Dung Tran, Feiyang Cei, Diego Manzanas Lopez, Taylor T.Johnson, Xenofon Koutsoukos, "Safety Verification of Cyber-Physical Systems with Reinforcement Learning Control", The International Conference on Embedded Software (EMSOFT), New York, October 2019. Acceptance Rate 25%. [http://taylortjohnson.com/research/tran2019emsoft.pdf]

• Hoang-Dung Tran, Patrick Musau, Diego Manzanas Lopez, Xiaodong Yang, Luan Viet Nguyen, Weiming Xiang, Taylor T.Johnson, "Parallelizable Reachability Analysis Algorithms for FeedForward Neural Networks", In 7th International Conference on Formal Methods in Software Engineering (FormaLISE), 27, May 2019 in Montreal, Canada, Acceptance Rate 28%. [http://taylortjohnson.com/research/tran2019formalise.pdf]

• Diego Manzanas Lopez, Patrick Musau, Hoang-Dung Tran, Taylor T.Johnson, "Verification of Closed-loop Systems with Neural Network Controllers (Benchmark Proposal)", The 6th International Workshop on Applied Verification of Continuous and Hybrid Systems (ARCH2019). Montreal, Canada, 2019. [http://taylortjohnson.com/research/lopez2019arch.pdf]

• Weiming Xiang, Hoang-Dung Tran, Taylor T. Johnson, "Output Reachable Set Estimation and Verification for Multi-Layer Neural Networks", In IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2018, March. [http://taylortjohnson.com/research/xiang2018tnnls.pdf]

• Weiming Xiang, Hoang-Dung Tran, Taylor T. Johnson, "Reachable Set Computation and Safety Verification for Neural Networks with ReLU Activations", In In Submission, IEEE, 2018, September. [http://www.taylortjohnson.com/research/xiang2018tcyb.pdf]

• Weiming Xiang, Diego Manzanas Lopez, Patrick Musau, Taylor T. Johnson, "Reachable Set Estimation and Verification for Neural Network Models of Nonlinear Dynamic Systems", In Unmanned System Technologies: Safe, Autonomous and Intelligent Vehicles, Springer, 2018, September. [http://www.taylortjohnson.com/research/xiang2018ust.pdf]

• Reachability Analysis and Safety Verification for Neural Network Control Systems, Weiming Xiang, Taylor T. Johnson [https://arxiv.org/abs/1805.09944]

• Weiming Xiang, Patrick Musau, Ayana A. Wild, Diego Manzanas Lopez, Nathaniel Hamilton, Xiaodong Yang, Joel Rosenfeld, Taylor T. Johnson, "Verification for Machine Learning, Autonomy, and Neural Networks Survey," October 2018, [https://arxiv.org/abs/1810.01989]

• Specification-Guided Safety Verification for Feedforward Neural Networks, Weiming Xiang, Hoang-Dung Tran, Taylor T. Johnson [https://arxiv.org/abs/1812.06161]

This work is supported in part by AFOSR, DARPA, NSF.

For any questions related to NNV, please add them to the issues or contact Diego Manzanas Lopez or Hoang Dung Tran.