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Study on influence of topology in DNN against adversarial attacks

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dc.contributor.authorShamshiri, Samaneh-
dc.contributor.authorSohn, Insoo-
dc.contributor.authorCho, Juphil-
dc.date.accessioned2025-10-28T04:30:22Z-
dc.date.available2025-10-28T04:30:22Z-
dc.date.issued2025-
dc.identifier.issn2165-8528-
dc.identifier.issn2165-8536-
dc.identifier.urihttps://scholarworks.dongguk.edu/handle/sw.dongguk/61883-
dc.description.abstractIn this paper, we introduce a novel complex network optimization method based on no-regret learning technique. Simulation results indicate that the proposed method obtains high robustness performance compared to conventional methods. © 2025 Elsevier B.V., All rights reserved.-
dc.format.extent3-
dc.language영어-
dc.language.isoENG-
dc.publisherIEEE-
dc.titleStudy on influence of topology in DNN against adversarial attacks-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1109/ICUFN65838.2025.11170013-
dc.identifier.scopusid2-s2.0-105018740242-
dc.identifier.bibliographicCitation2025 Sixteenth International Conference on Ubiquitous and Future Networks (ICUFN), pp 147 - 149-
dc.citation.title2025 Sixteenth International Conference on Ubiquitous and Future Networks (ICUFN)-
dc.citation.startPage147-
dc.citation.endPage149-
dc.type.docTypeConference paper-
dc.description.isOpenAccessY-
dc.description.journalRegisteredClassforeign-
dc.subject.keywordAuthorComplex Networks-
dc.subject.keywordAuthorNetwork Robustness-
dc.subject.keywordAuthorNoRegret Learning-
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