Evaluation of deepfake detection using YOLO with local binary pattern histogram
Status PubMed-not-MEDLINE Jazyk angličtina Země Spojené státy americké Médium electronic-ecollection
Typ dokumentu časopisecké články
PubMed
36262154
PubMed Central
PMC9575844
DOI
10.7717/peerj-cs.1086
PII: cs-1086
Knihovny.cz E-zdroje
- Klíčová slova
- Celeb DF-Face Forensics++, Celeb-DF, Deepfake, FaceForencies++, LBPH, YOLO,
- Publikační typ
- časopisecké články MeSH
Recently, deepfake technology has become a popularly used technique for swapping faces in images or videos that create forged data to mislead society. Detecting the originality of the video is a critical process due to the negative pattern of the image. In the detection of forged images or videos, various image processing techniques were implemented. Existing methods are ineffective in detecting new threats or false images. This article has proposed You Only Look Once-Local Binary Pattern Histogram (YOLO-LBPH) to detect fake videos. YOLO is used to detect the face in an image or a frame of a video. The spatial features are extracted from the face image using a EfficientNet-B5 method. Spatial feature extractions are fed as input in the Local Binary Pattern Histogram to extract temporal features. The proposed YOLO-LBPH is implemented using the large scale deepfake forensics (DF) dataset known as CelebDF-FaceForensics++(c23), which is a combination of FaceForensics++(c23) and Celeb-DF. As a result, the precision score is 86.88% in the CelebDF-FaceForensics++(c23) dataset, 88.9% in the DFFD dataset, 91.35% in the CASIA-WebFace data. Similarly, the recall is 92.45% in the Celeb-DF-Face Forensics ++(c23) dataset, 93.76% in the DFFD dataset, and 94.35% in the CASIA-Web Face dataset.
Department of Applied Cybernetics University of Hradec Králové Hradec Králové Czech Republic
Department of Computing Singidunum University Belgrade Serbia
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