DeDiM: De-identification using a diffusion model
| dc.contributor.author | Hidetsugu Uchida, Narishige Abe and Shigefumi Yamada | |
| dc.contributor.editor | Brömme, Arslan | |
| dc.contributor.editor | Damer, Naser | |
| dc.contributor.editor | Gomez-Barrero, Marta | |
| dc.contributor.editor | Raja, Kiran | |
| dc.contributor.editor | Rathgeb, Christian | |
| dc.contributor.editor | Sequeira Ana F. | |
| dc.contributor.editor | Todisco, Massimiliano | |
| dc.contributor.editor | Uhl, Andreas | |
| dc.date.accessioned | 2022-10-27T10:19:33Z | |
| dc.date.available | 2022-10-27T10:19:33Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | As a countermeasure against malicious authentication in a face recognition system using a face image obtained from SNS or the like, de-identification methods based on adversarial example have been studied. However, since adversarial example directly uses the gradient information of a face recognition model, it is highly dependent on the model, and a de-identification effect and image quality are difficult to achieve for an unknown recognition model. In this study, we propose a novel de-identification method based on a diffusion model, which has high generalizability to an unknown recognition model by applying minute changes to face shapes. Experiments using LFW showed that the proposed method has a higher de-identification effect for unknown models and better image quality than a conventional method using adversarial example. | en |
| dc.identifier.doi | 10.1109/BIOSIG55365.2022.9896972 | |
| dc.identifier.isbn | 978-3-88579-723-4 | |
| dc.identifier.pissn | 1617-5474 | |
| dc.identifier.uri | https://dl.gi.de/handle/20.500.12116/39717 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | BIOSIG 2022 | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-329 | |
| dc.subject | Face recognition | |
| dc.subject | de-identification | |
| dc.subject | diffusion model | |
| dc.title | DeDiM: De-identification using a diffusion model | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 79 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 72 | |
| gi.conference.date | 14.-16. September 2022 | |
| gi.conference.location | Darmstadt | |
| gi.conference.sessiontitle | Regular Research Papers |
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