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A Generalizable Deepfake Detector based on Neural Conditional Distribution Modelling

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dc.contributor.authorKhodabakhsh, Ali
dc.contributor.authorBusch, Christoph
dc.contributor.editorBrömme, Arslan
dc.contributor.editorBusch, Christoph
dc.contributor.editorDantcheva, Antitza
dc.contributor.editorRaja, Kiran
dc.contributor.editorRathgeb, Christian
dc.contributor.editorUhl, Andreas
dc.date.accessioned2020-09-16T08:25:44Z
dc.date.available2020-09-16T08:25:44Z
dc.date.issued2020
dc.description.abstractPhoto- and video-realistic generation techniques have become a reality following the advent of deep neural networks. Consequently, there are immense concerns regarding the difficulty in differentiating what content is real from what is synthetic. An example of video-realistic generation techniques is the infamous Deepfakes, which exploit the main modality by which humans identify each other. Deepfakes are a category of synthetic face generation methods and are commonly based on generative adversarial networks. In this article, we propose a novel two-step synthetic face image detection method in which general-purpose features are extracted in a first step, trivializing the task of detecting synthetic images. The anomaly detector predicts the conditional probabilities for observing every individual pixel in the image and is trained on pristine data only. The extracted anomaly features demonstrate true generalization capacity across widely different unknown synthesis methods while showing a minimal loss in performance with regard to the detection of known synthetic samples.en
dc.identifier.isbn978-3-88579-700-5
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/34326
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofBIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-306
dc.subjectDeepfake
dc.subjectVideo Forensics
dc.subjectGenerative Adversarial Networks
dc.subjectPixelCNN
dc.subjectUniversal Background Model.
dc.titleA Generalizable Deepfake Detector based on Neural Conditional Distribution Modellingen
dc.typeText/Conference Paper
gi.citation.endPage198
gi.citation.publisherPlaceBonn
gi.citation.startPage191
gi.conference.date16.-18. September 2020
gi.conference.locationInternational Digital Conference
gi.conference.sessiontitleFurther Conference Contributions

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P306 - BIOSIG 2020 - Proceedings of the 19th International Conference of the Biometrics Special Interest Group