Media Forensics Considerations on DeepFake Detection with Hand-Crafted Features
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| Název: | Media Forensics Considerations on DeepFake Detection with Hand-Crafted Features |
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| Autoři: | Dennis Siegel, Christian Kraetzer, Stefan Seidlitz, Jana Dittmann |
| Zdroj: | Journal of Imaging, Vol 7, Iss 108, p 108 (2021) |
| Informace o vydavateli: | MDPI AG |
| Rok vydání: | 2021 |
| Sbírka: | Directory of Open Access Journals: DOAJ Articles |
| Témata: | 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 |
| Popis: | 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. |
| Druh dokumentu: | article in journal/newspaper |
| Jazyk: | 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 |
| Dostupnost: | https://doi.org/10.3390/jimaging7070108 https://doaj.org/article/108e0bbc90934c71933aa00409ed0066 |
| Přístupové číslo: | edsbas.D14B5747 |
| Databáze: | BASE |
Nájsť tento článok vo Web of Science | Abstrakt: | 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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| DOI: | 10.3390/jimaging7070108 |