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Media Forensics Considerations on DeepFake Detection with Hand-Crafted Features

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Titel: Media Forensics Considerations on DeepFake Detection with Hand-Crafted Features
Autoren: Dennis Siegel, Christian Kraetzer, Stefan Seidlitz, Jana Dittmann
Quelle: Journal of Imaging, Vol 7, Iss 108, p 108 (2021)
Verlagsinformationen: MDPI AG
Publikationsjahr: 2021
Bestand: Directory of Open Access Journals: DOAJ Articles
Schlagwörter: DeepFake detection, hand-crafted features, forensic process model, plausibility of decisions, Photography, TR1-1050, Computer applications to medicine. Medical informatics, R858-859.7, Electronic computers. Computer science, QA75.5-76.95
Beschreibung: DeepFake detection is a novel task for media forensics and is currently receiving a lot of research attention due to the threat these targeted video manipulations propose to the trust placed in video footage. The current trend in DeepFake detection is the application of neural networks to learn feature spaces that allow them to be distinguished from unmanipulated videos. In this paper, we discuss, with features hand-crafted by domain experts, an alternative to this trend. The main advantage that hand-crafted features have over learned features is their interpretability and the consequences this might have for plausibility validation for decisions made. Here, we discuss three sets of hand-crafted features and three different fusion strategies to implement DeepFake detection. Our tests on three pre-existing reference databases show detection performances that are under comparable test conditions (peak AUC > 0.95) to those of state-of-the-art methods using learned features. Furthermore, our approach shows a similar, if not better, generalization behavior than neural network-based methods in tests performed with different training and test sets. In addition to these pattern recognition considerations, first steps of a projection onto a data-centric examination approach for forensics process modeling are taken to increase the maturity of the present investigation.
Publikationsart: article in journal/newspaper
Sprache: English
Relation: https://www.mdpi.com/2313-433X/7/7/108; https://doaj.org/toc/2313-433X; https://doaj.org/article/108e0bbc90934c71933aa00409ed0066
DOI: 10.3390/jimaging7070108
Verfügbarkeit: https://doi.org/10.3390/jimaging7070108
https://doaj.org/article/108e0bbc90934c71933aa00409ed0066
Dokumentencode: edsbas.D14B5747
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  – Url: https://doi.org/10.3390/jimaging7070108#
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  Data: Media Forensics Considerations on DeepFake Detection with Hand-Crafted Features
– Name: Author
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  Data: <searchLink fieldCode="AR" term="%22Dennis+Siegel%22">Dennis Siegel</searchLink><br /><searchLink fieldCode="AR" term="%22Christian+Kraetzer%22">Christian Kraetzer</searchLink><br /><searchLink fieldCode="AR" term="%22Stefan+Seidlitz%22">Stefan Seidlitz</searchLink><br /><searchLink fieldCode="AR" term="%22Jana+Dittmann%22">Jana Dittmann</searchLink>
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  Data: Journal of Imaging, Vol 7, Iss 108, p 108 (2021)
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  Data: MDPI AG
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  Data: 2021
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  Data: Directory of Open Access Journals: DOAJ Articles
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  Data: <searchLink fieldCode="DE" term="%22DeepFake+detection%22">DeepFake detection</searchLink><br /><searchLink fieldCode="DE" term="%22hand-crafted+features%22">hand-crafted features</searchLink><br /><searchLink fieldCode="DE" term="%22forensic+process+model%22">forensic process model</searchLink><br /><searchLink fieldCode="DE" term="%22plausibility+of+decisions%22">plausibility of decisions</searchLink><br /><searchLink fieldCode="DE" term="%22Photography%22">Photography</searchLink><br /><searchLink fieldCode="DE" term="%22TR1-1050%22">TR1-1050</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+applications+to+medicine%2E+Medical+informatics%22">Computer applications to medicine. Medical informatics</searchLink><br /><searchLink fieldCode="DE" term="%22R858-859%2E7%22">R858-859.7</searchLink><br /><searchLink fieldCode="DE" term="%22Electronic+computers%2E+Computer+science%22">Electronic computers. Computer science</searchLink><br /><searchLink fieldCode="DE" term="%22QA75%2E5-76%2E95%22">QA75.5-76.95</searchLink>
– Name: Abstract
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  Data: DeepFake detection is a novel task for media forensics and is currently receiving a lot of research attention due to the threat these targeted video manipulations propose to the trust placed in video footage. The current trend in DeepFake detection is the application of neural networks to learn feature spaces that allow them to be distinguished from unmanipulated videos. In this paper, we discuss, with features hand-crafted by domain experts, an alternative to this trend. The main advantage that hand-crafted features have over learned features is their interpretability and the consequences this might have for plausibility validation for decisions made. Here, we discuss three sets of hand-crafted features and three different fusion strategies to implement DeepFake detection. Our tests on three pre-existing reference databases show detection performances that are under comparable test conditions (peak AUC > 0.95) to those of state-of-the-art methods using learned features. Furthermore, our approach shows a similar, if not better, generalization behavior than neural network-based methods in tests performed with different training and test sets. In addition to these pattern recognition considerations, first steps of a projection onto a data-centric examination approach for forensics process modeling are taken to increase the maturity of the present investigation.
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  Data: 10.3390/jimaging7070108
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  Data: https://doi.org/10.3390/jimaging7070108<br />https://doaj.org/article/108e0bbc90934c71933aa00409ed0066
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      – Text: English
    Subjects:
      – SubjectFull: DeepFake detection
        Type: general
      – SubjectFull: hand-crafted features
        Type: general
      – SubjectFull: forensic process model
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      – SubjectFull: plausibility of decisions
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      – SubjectFull: Photography
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      – SubjectFull: TR1-1050
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      – SubjectFull: QA75.5-76.95
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      – TitleFull: Media Forensics Considerations on DeepFake Detection with Hand-Crafted Features
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