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Extending Neural Network Verification to a Larger Family of Piece-wise Linear Activation Functions

arXiv 2311.10780 ↗First posted 16 Nov 2023cs.LG cs.AI cs.LOcited by 0

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Abstract (arXiv)

In this paper, we extend an available neural network verification technique to support a wider class of piece-wise linear activation functions. Furthermore, we extend the algorithms, which provide in their original form exact respectively over-approximative results for bounded input sets represented as start sets, to allow also unbounded input set. We implemented our algorithms and demonstrated their effectiveness in some case studies.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 21 of its 59 references that are in the map. First posted on arXiv 16 Nov 2023.

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References · 21

  1. 2014Explaining and Harnessing Adversarial Examplescited by 7,364
  2. 2017Reluplex: An Efficient SMT Solver for Verifying Deep Neural Networkscited by 744
  3. 2016Safety Verification of Deep Neural Networkscited by 263
  4. 2017Formal Verification of Piece-Wise Linear Feed-Forward Neural Networkscited by 242
  5. 2019Algorithms for Verifying Deep Neural Networkscited by 221
  6. 2018Efficient Formal Safety Analysis of Neural Networkscited by 165
  7. 2021Deep physical neural networks enabled by a backpropagation algorithm for arbitrary physical systemscited by 128
  8. 2017An approach to reachability analysis for feed-forward ReLU neural networkscited by 127
  9. 2019A Survey of Deep Learning Applications to Autonomous Vehicle Controlcited by 106
  10. 2017Maximum Resilience of Artificial Neural Networkscited by 97
  11. 2018Reachability Analysis of Deep Neural Networks with Provable Guaranteescited by 93
  12. 2017Output Reachable Set Estimation and Verification for Multi-Layer Neural Networkscited by 80
9 more
  1. 2018Deep Neural Network Compression for Aircraft Collision Avoidance Systemscited by 58
  2. 2018CNN-Cert: An Efficient Framework for Certifying Robustness of Convolutional Neural Networkscited by 50
  3. 2013Scalable Object Detection using Deep Neural Networkscited by 31
  4. 2022Efficient Neural Network Analysis with Sum-of-Infeasibilitiescited by 24
  5. 2020Fast Geometric Projections for Local Robustness Certificationcited by 21
  6. 2017Intriguing Properties of Adversarial Examplescited by 20
  7. 2021Probabilistic Verification of Neural Networks Against Group Fairnesscited by 17
  8. 2020A Survey on Activation Functions and their relation with Xavier and He Normal Initializationcited by 13
  9. 2022Optimized Symbolic Interval Propagation for Neural Network Verificationcited by 4

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