Conference item
Safety verification for deep neural networks with provable guarantees
- Abstract:
- Computing systems are becoming ever more complex, increasingly often incorporating deep learning components. Since deep learning is unstable with respect to adversarial perturbations, there is a need for rigorous software development methodologies that encompass machine learning. This paper describes progress with developing automated verification techniques for deep neural networks to ensure safety and robustness of their decisions with respect to input perturbations. This includes novel algorithms based on feature-guided search, games, global optimisation and Bayesian methods.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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- Files:
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(Preview, Version of record, pdf, 1.4MB, Terms of use)
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- Publisher copy:
- 10.4230/LIPIcs.CONCUR.2019.1
Authors
- Publisher:
- Leibniz International Proceedings in Informatics, LIPIcs
- Host title:
- International Conference on Concurrency Theory (CONCUR 2019)
- Journal:
- 30th International Conference on Concurrency Theory (CONCUR 2019) More from this journal
- Volume:
- 140
- Pages:
- 1:1-1:5
- Publication date:
- 2019-08-01
- Acceptance date:
- 2019-07-06
- DOI:
- ISSN:
-
1868-8969
- ISBN:
- 9783959771214
- Keywords:
- Pubs id:
-
pubs:1035778
- UUID:
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uuid:5866ee47-a875-4c93-bd89-1a9352bfe10f
- Local pid:
-
pubs:1035778
- Source identifiers:
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1035778
- Deposit date:
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2019-10-25
Terms of use
- Copyright holder:
- Marta Z. Kwiatkowska
- Copyright date:
- 2019
- Notes:
- © Marta Z. Kwiatkowska; licensed under Creative Commons License CC-BY
- Licence:
- CC Attribution (CC BY)
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