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VNN: Verification-Friendly Neural Networks with Hard Robustness Guarantees

Baninajjar, Anahita LU orcid ; Rezine, Ahmed and Aminifar, Amir LU orcid (2024) 41st International Conference on Machine Learning, ICML 2024 In Proceedings of Machine Learning Research 235. p.2846-2856
Abstract
Machine learning techniques often lack formal correctness guarantees, evidenced by the widespread adversarial examples that plague most deep-learning applications. This lack of formal guarantees resulted in several research efforts that aim at verifying Deep Neural Networks (DNNs), with a particular focus on safety-critical applications. However, formal verification techniques still face major scalability and precision challenges. The over-approximation introduced during the formal verification process to tackle the scalability challenge often results in inconclusive analysis. To address this challenge, we propose a novel framework to generate Verification-Friendly Neural Networks (VNNs). We present a post-training optimization framework... (More)
Machine learning techniques often lack formal correctness guarantees, evidenced by the widespread adversarial examples that plague most deep-learning applications. This lack of formal guarantees resulted in several research efforts that aim at verifying Deep Neural Networks (DNNs), with a particular focus on safety-critical applications. However, formal verification techniques still face major scalability and precision challenges. The over-approximation introduced during the formal verification process to tackle the scalability challenge often results in inconclusive analysis. To address this challenge, we propose a novel framework to generate Verification-Friendly Neural Networks (VNNs). We present a post-training optimization framework to achieve a balance between preserving prediction performance and verification-friendliness. Our proposed framework results in VNNs that are comparable to the original DNNs in terms of prediction performance, while amenable to formal verification techniques. This essentially enables us to establish robustness for more VNNs than their DNN counterparts, in a time-efficient manner. (Less)
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author
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organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
Proceedings of the 41st International Conference on Machine Learning
series title
Proceedings of Machine Learning Research
volume
235
pages
2846 - 2856
publisher
ML Research Press
conference name
41st International Conference on Machine Learning, ICML 2024
conference location
Vienna, Austria
conference dates
2024-07-21 - 2024-07-27
ISSN
2640-3498
language
English
LU publication?
yes
id
51a9d3bd-b82e-4a7a-a501-a82f3031d544
alternative location
https://proceedings.mlr.press/v235/baninajjar24a.html
date added to LUP
2024-07-28 08:13:40
date last changed
2024-08-05 17:40:11
@inproceedings{51a9d3bd-b82e-4a7a-a501-a82f3031d544,
  abstract     = {{Machine learning techniques often lack formal correctness guarantees, evidenced by the widespread adversarial examples that plague most deep-learning applications. This lack of formal guarantees resulted in several research efforts that aim at verifying Deep Neural Networks (DNNs), with a particular focus on safety-critical applications. However, formal verification techniques still face major scalability and precision challenges. The over-approximation introduced during the formal verification process to tackle the scalability challenge often results in inconclusive analysis. To address this challenge, we propose a novel framework to generate Verification-Friendly Neural Networks (VNNs). We present a post-training optimization framework to achieve a balance between preserving prediction performance and verification-friendliness. Our proposed framework results in VNNs that are comparable to the original DNNs in terms of prediction performance, while amenable to formal verification techniques. This essentially enables us to establish robustness for more VNNs than their DNN counterparts, in a time-efficient manner.}},
  author       = {{Baninajjar, Anahita and Rezine, Ahmed and Aminifar, Amir}},
  booktitle    = {{Proceedings of the 41st International Conference on Machine Learning}},
  issn         = {{2640-3498}},
  language     = {{eng}},
  pages        = {{2846--2856}},
  publisher    = {{ML Research Press}},
  series       = {{Proceedings of Machine Learning Research}},
  title        = {{VNN: Verification-Friendly Neural Networks with Hard Robustness Guarantees}},
  url          = {{https://proceedings.mlr.press/v235/baninajjar24a.html}},
  volume       = {{235}},
  year         = {{2024}},
}