Application of complex systems in neural networks against Backdoor attacks

Sara Kaviani, Insoo Sohn, Huaping Liu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Through the success of artificial neural networks (ANNs) in different domains and their increasing computational complexities, third parties and MLaaS (machine learning as a service) has been vastly used to do the training procedure. Hence the high possibility for malicious training recently caused intense researches centered on making these ANNs robust against various types of attacks such as backdoors. Backdoor attacks makes the ANN to behave normally on clean data but causes targeted misclassification in presence of the trigger. In this paper we provide the first investigation about the influence of applying complex systems such as random and scale-free networks instead of fully-connected structures on the robustness of feed forward neural networks (FFANNs) against backdoor attacks.

Original languageEnglish
Title of host publicationICTC 2020 - 11th International Conference on ICT Convergence
Subtitle of host publicationData, Network, and AI in the Age of Untact
PublisherIEEE Computer Society
Pages57-59
Number of pages3
ISBN (Electronic)9781728167589
DOIs
StatePublished - 21 Oct 2020
Event11th International Conference on Information and Communication Technology Convergence, ICTC 2020 - Jeju Island, Korea, Republic of
Duration: 21 Oct 202023 Oct 2020

Publication series

NameInternational Conference on ICT Convergence
Volume2020-October
ISSN (Print)2162-1233
ISSN (Electronic)2162-1241

Conference

Conference11th International Conference on Information and Communication Technology Convergence, ICTC 2020
Country/TerritoryKorea, Republic of
CityJeju Island
Period21/10/2023/10/20

Keywords

  • Backdoor attacks
  • Feed forward neural networks
  • Robustness

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