TITLE:
Deepfake Video Detection Employing Human Facial Features
AUTHORS:
Daniel Schilling Weiss Nguyen, Desmond T. Ademiluyi
KEYWORDS:
Artificial Intelligence, Convoluted Neural Networks, Deepfake, GANs, Generalization, Deep Learning, Facial Features, Video Frames
JOURNAL NAME:
Journal of Computer and Communications,
Vol.11 No.12,
December
7,
2023
ABSTRACT: Deepfake technology can be used to replace people’s faces in videos or pictures to show them saying or doing things they never said or did. Deepfake media are often used to extort, defame, and manipulate public opinion. However, despite deepfake technology’s risks, current deepfake detection methods lack generalization and are inconsistent when applied to unknown videos, i.e., videos on which they have not been trained. The purpose of this study is to develop a generalizable deepfake detection model by training convoluted neural networks (CNNs) to classify human facial features in videos. The study formulated the research questions: “How effectively does the developed model provide reliable generalizations?” A CNN model was trained to distinguish between real and fake videos using the facial features of human subjects in videos. The model was trained, validated, and tested using the FaceForensiq++ dataset, which contains more than 500,000 frames and subsets of the DFDC dataset, totaling more than 22,000 videos. The study demonstrated high generalizability, as the accuracy of the unknown dataset was only marginally (about 1%) lower than that of the known dataset. The findings of this study indicate that detection systems can be more generalizable, lighter, and faster by focusing on just a small region (the human face) of an entire video.