Smart Redundancy Schemes for ANNs against Fault Attacks

Troya Çağıl Köylü, Said Hamdioui, Mottaqiallah Taouil

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

1 Citation (Scopus)
39 Downloads (Pure)


Artificial neural networks (ANNs) are used to accomplish a variety of tasks, including safety critical ones. Hence, it is important to protect them against faults that can influence decisions during operation. In this paper, we propose smart and low-cost redundancy schemes that protect the most vulnerable ANN parts against fault attacks. Experimental results show that the two proposed smart schemes perform similarly to dual modular redundancy (DMR) at a much lower cost, generally improve on the state of the art, and reach protection levels in the range of 93% to 99%.
Original languageEnglish
Title of host publicationProceedings of the 2022 IEEE European Test Symposium (ETS)
Place of PublicationDanvers
Number of pages2
ISBN (Electronic)978-1-6654-6706-3
ISBN (Print)978-1-6654-6707-0
Publication statusPublished - 2022
Event2022 IEEE European Test Symposium (ETS) - Barcelona, Spain
Duration: 23 May 202227 May 2022


Conference2022 IEEE European Test Symposium (ETS)

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.


  • artificial neural network
  • redundancy
  • fault injection
  • countermeasure
  • machine learning


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