DoFNet: Depth of Field Difference Learning for Detecting Image Forgery
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Jeong, Y. | - |
dc.contributor.author | Choi, J. | - |
dc.contributor.author | Kim, D. | - |
dc.contributor.author | Park, S. | - |
dc.contributor.author | Hong, M. | - |
dc.contributor.author | Park, C. | - |
dc.contributor.author | Min, S. | - |
dc.contributor.author | Gwon, Y. | - |
dc.date.accessioned | 2021-06-18T07:14:16Z | - |
dc.date.available | 2021-06-18T07:14:16Z | - |
dc.date.issued | 2021-02 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/44122 | - |
dc.description.abstract | Recently, online transactions have had an exponential growth and expanded to various cases, such as opening bank accounts and filing for insurance claims. Despite the effort of many companies requiring their own mobile applications to capture images for online transactions, it is difficult to restrict users from taking a picture of other’s images displayed on a screen. To detect such cases, we propose a novel approach using paired images with different depth of field (DoF) for distinguishing the real images and the display images. Also, we introduce a new dataset containing 2,752 pairs of images capturing real and display objects on various types of displays, which is the largest real dataset employing DoF with multi-focus. Furthermore, we develop a new framework to concentrate on the difference of DoF in paired images, while avoiding learning individual display artifacts. Since DoF lies on the optical fundamentals, the framework can be widely utilized with any camera, and its performance shows at least 23 % improvement compared to the conventional classification models. © 2021, Springer Nature Switzerland AG. | - |
dc.format.extent | 18 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Springer Science and Business Media Deutschland GmbH | - |
dc.title | DoFNet: Depth of Field Difference Learning for Detecting Image Forgery | - |
dc.type | Article | - |
dc.identifier.doi | 10.1007/978-3-030-69544-6_6 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.12627 LNCS, pp 83 - 100 | - |
dc.description.isOpenAccess | N | - |
dc.identifier.scopusid | 2-s2.0-85103291173 | - |
dc.citation.endPage | 100 | - |
dc.citation.startPage | 83 | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 12627 LNCS | - |
dc.type.docType | Conference Paper | - |
dc.publisher.location | 독일 | - |
dc.subject.keywordPlus | Insurance | - |
dc.subject.keywordPlus | Capture images | - |
dc.subject.keywordPlus | Classification models | - |
dc.subject.keywordPlus | Depth of field | - |
dc.subject.keywordPlus | Display image | - |
dc.subject.keywordPlus | Exponential growth | - |
dc.subject.keywordPlus | Insurance claims | - |
dc.subject.keywordPlus | Mobile applications | - |
dc.subject.keywordPlus | Online transaction | - |
dc.subject.keywordPlus | Computer vision | - |
dc.description.journalRegisteredClass | scopus | - |
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