Deep learning for deepfakes creation and detection: A survey
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Thanh Thi Nguyen | - |
dc.contributor.author | Quoc Viet Hung Nguyen | - |
dc.contributor.author | Dung Tien Nguyen | - |
dc.contributor.author | Duc Thanh Nguyen | - |
dc.contributor.author | Thien Huynh-The | - |
dc.contributor.author | Nahavandi, Saeid | - |
dc.contributor.author | Thanh Tam Nguyen | - |
dc.contributor.author | Quoc-Viet Pham | - |
dc.contributor.author | Nguyen, Cuong M. | - |
dc.date.accessioned | 2024-02-27T16:32:15Z | - |
dc.date.available | 2024-02-27T16:32:15Z | - |
dc.date.issued | 2022-10 | - |
dc.identifier.issn | 1077-3142 | - |
dc.identifier.issn | 1090-235X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/28407 | - |
dc.description.abstract | Deep learning has been successfully applied to solve various complex problems ranging from big data analytics to computer vision and human-level control. Deep learning advances however have also been employed to create software that can cause threats to privacy, democracy and national security. One of those deep learningpowered applications recently emerged is deepfake. Deepfake algorithms can create fake images and videos that humans cannot distinguish them from authentic ones. The proposal of technologies that can automatically detect and assess the integrity of digital visual media is therefore indispensable. This paper presents a survey of algorithms used to create deepfakes and, more importantly, methods proposed to detect deepfakes in the literature to date. We present extensive discussions on challenges, research trends and directions related to deepfake technologies. By reviewing the background of deepfakes and state-of-the-art deepfake detection methods, this study provides a comprehensive overview of deepfake techniques and facilitates the development of new and more robust methods to deal with the increasingly challenging deepfakes. | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | ACADEMIC PRESS INC ELSEVIER SCIENCE | - |
dc.title | Deep learning for deepfakes creation and detection: A survey | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1016/j.cviu.2022.103525 | - |
dc.identifier.wosid | 000857055600005 | - |
dc.identifier.bibliographicCitation | COMPUTER VISION AND IMAGE UNDERSTANDING, v.223 | - |
dc.citation.title | COMPUTER VISION AND IMAGE UNDERSTANDING | - |
dc.citation.volume | 223 | - |
dc.type.docType | Article | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence | - |
dc.relation.journalWebOfScienceCategory | Engineering, Electrical & Electronic | - |
dc.subject.keywordPlus | GENERATIVE ADVERSARIAL NETWORKS | - |
dc.subject.keywordPlus | FORGERY DETECTION | - |
dc.subject.keywordPlus | IMAGE | - |
dc.subject.keywordPlus | REPRESENTATION | - |
dc.subject.keywordPlus | AGE | - |
dc.subject.keywordAuthor | Deepfakes | - |
dc.subject.keywordAuthor | Face manipulation | - |
dc.subject.keywordAuthor | Artificial intelligence | - |
dc.subject.keywordAuthor | Deep learning | - |
dc.subject.keywordAuthor | Autoencoders | - |
dc.subject.keywordAuthor | GAN | - |
dc.subject.keywordAuthor | Forensics | - |
dc.subject.keywordAuthor | Survey | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
350-27, Gumi-daero, Gumi-si, Gyeongsangbuk-do, Republic of Korea (39253)054-478-7170
COPYRIGHT 2020 Kumoh University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.