Adversarial Example Detection Based on Improved GhostBusters
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, H. | - |
dc.contributor.author | Shin, J. | - |
dc.contributor.author | Jo, H.J. | - |
dc.date.accessioned | 2023-03-21T02:40:05Z | - |
dc.date.available | 2023-03-21T02:40:05Z | - |
dc.date.created | 2023-01-02 | - |
dc.date.issued | 2022-11 | - |
dc.identifier.issn | 0916-8532 | - |
dc.identifier.uri | http://scholarworks.bwise.kr/ssu/handle/2018.sw.ssu/43413 | - |
dc.description.abstract | In various studies of attacks on autonomous vehicles (AVs), a phantom attack in which advanced driver assistance system (ADAS) misclassifies a fake object created by an adversary as a real object has been proposed. In this paper, we propose F-GhostBusters, which is an improved version of GhostBusters that detects phantom attacks. The proposed model uses a new feature, i.e, frequency of images. Experimental results show that F-GhostBusters not only improves the detection performance of GhostBusters but also can complement the accuracy against adversarial examples. © 2022 The Institute of Electronics, Information and Communication Engineers. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Institute of Electronics Information Communication Engineers | - |
dc.relation.isPartOf | IEICE Transactions on Information and Systems | - |
dc.title | Adversarial Example Detection Based on Improved GhostBusters | - |
dc.type | Article | - |
dc.identifier.doi | 10.1587/transinf.2022NGL0005 | - |
dc.type.rims | ART | - |
dc.identifier.bibliographicCitation | IEICE Transactions on Information and Systems, v.E105D, no.11, pp.1921 - 1922 | - |
dc.description.journalClass | 1 | - |
dc.identifier.wosid | 000937990900011 | - |
dc.identifier.scopusid | 2-s2.0-85141865548 | - |
dc.citation.endPage | 1922 | - |
dc.citation.number | 11 | - |
dc.citation.startPage | 1921 | - |
dc.citation.title | IEICE Transactions on Information and Systems | - |
dc.citation.volume | E105D | - |
dc.contributor.affiliatedAuthor | Jo, H.J. | - |
dc.identifier.url | https://www.jstage.jst.go.jp/article/transinf/E105.D/11/E105.D_2022NGL0005/_article | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | Y | - |
dc.subject.keywordAuthor | adversarial examples | - |
dc.subject.keywordAuthor | convolutional neural network | - |
dc.subject.keywordAuthor | Fourier transformation | - |
dc.subject.keywordAuthor | image classification | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information SystemsComputer Science, Software Engineering | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
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