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dc.contributor.authorChau, Xuan Truong Du
dc.contributor.authorHoang, Duong Le
dc.contributor.authorThanh Trung, Huynh
dc.contributor.authorPham, Minh Tam
dc.contributor.authorNguyen, Quoc Viet Hung
dc.contributor.authorJo, Jun
dc.contributor.authorNguyen, Tam
dc.date.accessioned2021-02-09T04:46:49Z
dc.date.available2021-02-09T04:46:49Z
dc.date.issued2020
dc.identifier.doi10.1109/ssci47803.2020.9308305
dc.identifier.urihttp://hdl.handle.net/10072/401901
dc.description.abstractThe recent years have witnessed the significant development of visual forgery techniques and their malicious applications such as spreading of fake news and rumours, defamation or blackmailing of politicians and celebrities, manipulation of election result in political warfare. The manipulated contents have reached to such sophisticated level that human cannot tell apart whether a given content is real or fake. To deal with this serious threat, a rich body of visual forensic techniques has been proposed for detecting forged video and images. However, existing techniques either rely solely on engineered features or require a complex deep learning model to extract the underlying patterns. In this paper, we propose a novel end-to-end visual forensic framework that can incorporate different modalities to efficiently classify real and forged contents. The model employs both the original content and its frequency domain analysis to fully exploit the richness of the image latent patterns. They are forwarded into two separated EfficientNet, a light yet efficient neural network architecture specialized for image classification, for pattern extraction. Then, we design a late-fusion mechanism to fuse the learnt features in original and frequency domain based on the importance of the underlying information. Our experimental results show that our proposed technique outperforms other state-of-the-art forensic approaches in many datasets and being robust to various visual forgery techniques.
dc.description.peerreviewedYes
dc.publisherIEEE
dc.relation.ispartofconferencename18th Australasian Data Mining Conference 2020 (AUSDM'20)
dc.relation.ispartofconferencetitle2020 IEEE Symposium Series on Computational Intelligence (SSCI)
dc.relation.ispartofdatefrom2020-12-01
dc.relation.ispartofdateto2020-12-04
dc.relation.ispartoflocationCanberra, Australia
dc.subject.fieldofresearchArtificial intelligence
dc.subject.fieldofresearchcode4602
dc.titleEfficient-Frequency: A hybrid visual forensic framework for facial forgery detection
dc.typeConference output
dc.type.descriptionE1 - Conferences
dcterms.bibliographicCitationChau, XTD; Hoang, DL; Thanh Trung, H; Pham, MT; Nguyen, QVH; Jo, J; Nguyen, T, Efficient-Frequency: A hybrid visual forensic framework for facial forgery detection, 2020 IEEE Symposium Series on Computational Intelligence (SSCI), 2020
dc.date.updated2021-02-09T04:44:45Z
dc.description.versionAccepted Manuscript (AM)
gro.rights.copyright© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.hasfulltextFull Text
gro.griffith.authorNguyen, Henry
gro.griffith.authorChau, Xuan Truong Du
gro.griffith.authorJo, Jun
gro.griffith.authorThanh Trung, Huynh


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