Abstract
With the milliards of images and videos, visual content has become a critical source of information. The spread of misinformation has become a significant problem through the availability of editing tools, requiring robust manipulation detection methods. Some of the manipulations, such as copy-move and splicing, are easy to detect, while other advanced facial manipulations, such as DeepFake are hard to detect. Facial manipulations can change human expressions by creating highly realistic faces. In this paper, we propose an efficient method to expose DeepFake in digital videos. A fusion of hand-crafted and deep-learned features is utilized to improve detection performance. The image quality measure (FM) is used besides the similarity measure of face and body skin color to generate the hand-crafted features. The experimental results show the efficiency of the proposed method for exposing DeepFake. We conducted the experiments on the three commonly and publicly available datasets Celeb-df, DFDC, and Faceforensics++.
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Data Availability
The datasets used and/or analyzed during the current study are available in the Celeb-DF, DFDC, and FaceForensics++ repositories respectively,
http://www.cs.albany.edu/lsw/celeb-deepfakeforensics.html
Notes
Masayuki Tanaka (2021). Face Parts Detection https://www.mathworks.com/matlabcentral/fileexchange/36855-face-parts-detection, MATLAB Central File Exchange. Retrieved September 4, 2021.
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Acknowledgements
This work was supported by the National Natural Science Foundation of China [grant numbers 61771168, 61471141, 61361166006, 61571018, and 61531003]; Key Technology Program of Shenzhen, China, [grant number JSGG20160427185010977]; Basic Research Project of Shenzhen, China [grant number JCYJ20150513151706561].
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Megahed, A., Han, Q. & Fadl, S. Exposing deepfake using fusion of deep-learned and hand-crafted features. Multimed Tools Appl 83, 26797–26817 (2024). https://doi.org/10.1007/s11042-023-16329-2
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DOI: https://doi.org/10.1007/s11042-023-16329-2