Hubálovský Štěpán, Trojovský Pavel, Bacanin Nebojsa, K Venkatachalam
Department of Applied Cybernetics, University of Hradec Králové, Hradec Králové, Czech Republic.
Department of Mathematics, Faculty of Science, University of Hradec Králové, Hradec Kralove, Czech Republic.
PeerJ Comput Sci. 2022 Sep 13;8:e1086. doi: 10.7717/peerj-cs.1086. eCollection 2022.
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.
最近，深度伪造技术已成为一种广泛应用于图像或视频中人脸交换的技术，它能创建伪造数据以误导社会。由于图像的负面模式，检测视频的原创性是一个关键过程。在伪造图像或视频的检测中，人们实施了各种图像处理技术。现有方法在检测新威胁或虚假图像方面效果不佳。本文提出了You Only Look Once-局部二值模式直方图（YOLO-LBPH）来检测虚假视频。YOLO用于检测图像或视频帧中的人脸。使用EfficientNet-B5方法从人脸图像中提取空间特征。空间特征提取结果作为输入馈入局部二值模式直方图以提取时间特征。所提出的YOLO-LBPH是使用大规模深度伪造取证（DF）数据集CelebDF-FaceForensics++(c23)实现的，该数据集是FaceForensics++(c23)和Celeb-DF的组合。结果，在CelebDF-FaceForensics++(c23)数据集中精确率得分为86.88%，在DFFD数据集中为88.9%，在CASIA-WebFace数据中为91.35%。同样，在Celeb-DF-Face Forensics ++(c23)数据集中召回率为92.45%，在DFFD数据集中为93.76%，在CASIA-Web Face数据集中为94.35%。
