Domain Generalization for Face Forgery Detection by Style Transfer
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Taehoon | - |
dc.contributor.author | Choi, Jongwook | - |
dc.contributor.author | Cho, Hyunjin | - |
dc.contributor.author | Lim, Hyoungjun | - |
dc.contributor.author | Choi, Jongwon | - |
dc.date.accessioned | 2024-03-28T04:30:27Z | - |
dc.date.available | 2024-03-28T04:30:27Z | - |
dc.date.issued | 2024-01 | - |
dc.identifier.issn | 0747-668X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/73045 | - |
dc.description.abstract | Although deep fake detection models have made significant progress, the challenge of performance degradation remains yet for unseen datasets. To address this, we introduce a novel data generalization approach using style transfer to generate images in various domains. Utilizing style transfer, we create a new domain where domain-specific information is eliminated and subsequently train our model on the new domain. Our approach enhances the generalization performance of the detector by adding the style-transferred images to train the deepfake detector. Through the experiments, we confirm that the performance on the trained dataset remains unchanged while achieving an improvement of 8.8% on an unseen dataset. Therefore, We verify the effectiveness of the style-transferred images for generalizing the performance upon unseen datasets. © 2024 IEEE. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Domain Generalization for Face Forgery Detection by Style Transfer | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/ICCE59016.2024.10444215 | - |
dc.identifier.bibliographicCitation | Digest of Technical Papers - IEEE International Conference on Consumer Electronics, v.2024 IEEE | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85187006407 | - |
dc.citation.title | Digest of Technical Papers - IEEE International Conference on Consumer Electronics | - |
dc.citation.volume | 2024 IEEE | - |
dc.type.docType | Conference paper | - |
dc.subject.keywordAuthor | data augmentation | - |
dc.subject.keywordAuthor | Deepfake detection | - |
dc.subject.keywordAuthor | forgery detection | - |
dc.subject.keywordAuthor | style transfer | - |
dc.description.journalRegisteredClass | scopus | - |
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