Adversarial Neural Pruning with Modified Latent Vulnerability utilizing Feature Robustness
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lim, Hyuntak | - |
dc.contributor.author | Chung, Ki Seok | - |
dc.date.accessioned | 2022-07-06T11:33:37Z | - |
dc.date.available | 2022-07-06T11:33:37Z | - |
dc.date.created | 2022-03-07 | - |
dc.date.issued | 2021-11 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/140381 | - |
dc.description.abstract | Today, vision recognition with Convolutional Neural Network (CNN) shows performance good enough to be employed in safety-critical autonomous driving. However, CNN models are vulnerable to adversarial attacks. To overcome this vulnerability, many methods have been studied Pruning is regarded as one of the effective methods to make the network more robust against adversarial attacks. In this paper, we introduce a new vulnerability loss to suppress the vulnerability better when the pruning is used to counter adversarial attacks. With this vulnerability suppression, we achieve up to 1.12% better accuracy against the adversarial examples compared to a previous study called ANP-VS. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | IEEE | - |
dc.title | Adversarial Neural Pruning with Modified Latent Vulnerability utilizing Feature Robustness | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Chung, Ki Seok | - |
dc.identifier.doi | 10.1109/ICCE-Asia53811.2021.9641893 | - |
dc.identifier.scopusid | 2-s2.0-85123757708 | - |
dc.identifier.wosid | 000766412700012 | - |
dc.identifier.bibliographicCitation | 2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021, pp.1 - 4 | - |
dc.relation.isPartOf | 2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021 | - |
dc.citation.title | 2021 IEEE International Conference on Consumer Electronics-Asia, ICCE-Asia 2021 | - |
dc.citation.startPage | 1 | - |
dc.citation.endPage | 4 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
dc.subject.keywordPlus | Convolutional neural networks | - |
dc.subject.keywordPlus | Safety engineering | - |
dc.subject.keywordPlus | Network security | - |
dc.subject.keywordPlus | Adversarial attack | - |
dc.subject.keywordPlus | Autonomous driving | - |
dc.subject.keywordPlus | Convolutional neural network | - |
dc.subject.keywordPlus | Neural network model | - |
dc.subject.keywordPlus | Performance | - |
dc.subject.keywordPlus | Vision recognition | - |
dc.subject.keywordPlus | Weight pruning | - |
dc.subject.keywordAuthor | Adversarial Attack | - |
dc.subject.keywordAuthor | Weight Pruning | - |
dc.identifier.url | https://ieeexplore.ieee.org/document/9641893 | - |
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