SpecRepair : Counter-Example Guided Safety Repair of Deep Neural Networks

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LEGUNSEN, Owolabi, ed., Grigore ROSU, ed.. Model Checking Software : 28th International Symposium, SPIN 2022, Virtual Event, May 21, 2022 : Proceedings. Cham: Springer, 2022, pp. 79-96. Lecture Notes in Computer Science. 13255. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-031-15076-0. Available under: doi: 10.1007/978-3-031-15077-7_5
Zusammenfassung

Deep neural networks (DNNs) are increasingly applied in safety-critical domains, such as self-driving cars, unmanned aircraft, and medical diagnosis. It is of fundamental importance to certify the safety of these DNNs, i.e. that they comply with a formal safety specification. While safety certification tools exactly answer this question, they are of no help in debugging unsafe DNNs, requiring the developer to iteratively verify and modify the DNN until safety is eventually achieved. Hence, a repair technique needs to be developed that can produce a safe DNN automatically. To address this need, we present SpecRepair, a tool that efficiently eliminates counter-examples from a DNN and produces a provably safe DNN without harming its classification accuracy. SpecRepair combines specification-based counter-example search and resumes training of the DNN, penalizing counter-examples and certifying the resulting DNN. We evaluate SpecRepair’s effectiveness on the ACAS Xu benchmark, a DNN-based controller for unmanned aircraft, and two image classification benchmarks. The results show that SpecRepair is more successful in producing safe DNNs than comparable methods, has a shorter runtime, and produces safe DNNs while preserving their classification accuracy.

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004 Informatik
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Neural networks, Safety repair, Safety specification
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Model Checking Software : 28th International Symposium, SPIN 2022 (virtual), 21. Mai 2022
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ISO 690BAUER-MARQUART, Fabian, David BOETIUS, Stefan LEUE, Christian SCHILLING, 2022. SpecRepair : Counter-Example Guided Safety Repair of Deep Neural Networks. Model Checking Software : 28th International Symposium, SPIN 2022 (virtual), 21. Mai 2022. In: LEGUNSEN, Owolabi, ed., Grigore ROSU, ed.. Model Checking Software : 28th International Symposium, SPIN 2022, Virtual Event, May 21, 2022 : Proceedings. Cham: Springer, 2022, pp. 79-96. Lecture Notes in Computer Science. 13255. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-031-15076-0. Available under: doi: 10.1007/978-3-031-15077-7_5
BibTex
@inproceedings{BauerMarquart2022SpecR-59172,
  year={2022},
  doi={10.1007/978-3-031-15077-7_5},
  title={SpecRepair : Counter-Example Guided Safety Repair of Deep Neural Networks},
  number={13255},
  isbn={978-3-031-15076-0},
  issn={0302-9743},
  publisher={Springer},
  address={Cham},
  series={Lecture Notes in Computer Science},
  booktitle={Model Checking Software : 28th International Symposium, SPIN 2022, Virtual Event, May 21, 2022 : Proceedings},
  pages={79--96},
  editor={Legunsen, Owolabi and Rosu, Grigore},
  author={Bauer-Marquart, Fabian and Boetius, David and Leue, Stefan and Schilling, Christian}
}
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