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Fake Face Detection Methods: Can They Be Generalized?

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dc.contributor.authorKhodabakhsh, Ali
dc.contributor.authorRamachandra, Raghavendra
dc.contributor.authorRaja, Kiran
dc.contributor.authorWasnik, Pankaj
dc.contributor.authorBusch, Christoph
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorRathgeb, Christian
dc.contributor.editorUhl, Andreas
dc.date.accessioned2019-06-17T10:00:29Z
dc.date.available2019-06-17T10:00:29Z
dc.date.issued2018
dc.description.abstractWith advancements in technology, it is now possible to create representations of human faces in a seamless manner for fake media, leveraging the large-scale availability of videos. These fake faces can be used to conduct personation attacks on the targeted subjects. Availability of open source software and a variety of commercial applications provides an opportunity to generate fake videos of a particular target subject in a number of ways. In this article, we evaluate the generalizability of the fake face detection methods through a series of studies to benchmark the detection accuracy. To this extent, we have collected a new database of more than 53;000 images, from 150 videos, originating from multiple sources of digitally generated fakes including Computer Graphics Image (CGI) generation and many tampering based approaches. In addition, we have also included images (with more than 3;200) from the predominantly used Swap-Face application that is commonly available on smart-phones. Extensive experiments are carried out using both texture-based handcrafted detection methods and deep learning based detection methods to find the suitability of detection methods. Through the set of evaluation, we attempt to answer if the current fake face detection methods can be generalizable.en
dc.identifier.isbn978-3-88579-676-4
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/23809
dc.language.isoen
dc.publisherKöllen Druck+Verlag GmbH
dc.relation.ispartofBIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-283
dc.subjectFake Face
dc.subjectPresentation Attack Detection
dc.subjectDataset
dc.subjectGeneralization
dc.subjectTransfer Learning.
dc.titleFake Face Detection Methods: Can They Be Generalized?en
dc.typeText/Conference Paper
gi.citation.publisherPlaceBonn
gi.conference.date26.-28. September 2018
gi.conference.locationDarmstadt

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P282 - BIOSIG 2018 - Proceedings of the 17th International Conference of the Biometrics Special Interest Group