Research and Analysis of Deepfake Video Generation and Detection

Abstract

Deepfake technology refers to the use of deep learning to generate fabricated audiovisual content. It can be utilized in virtual reality and movie production. However, if used for malicious purposes, it can impact individuals and society and pose national security issues. Governments worldwide have introduced policies and legislation to address these issues. Detecting deepfake videos is thus critically important. This study analyzes methods of generating and detecting deepfake videos. We used frameworks such as DeepFaceLab and FakeApp to produce deepfake videos. Employing the Xception depthwise separable convolution network model as the foundation, we adopted the FakeVideoForensics method for continuous video analysis, the DeepFakes_FacialRegions method for specific facial feature analysis, and the Improved Xception method, which showed better performance. We have developed an enhanced depthwise separable convolution and facial feature extraction method for deepfake video detection, named Improved Xception Feature Fake Video Detection (IXFFVD). This method was trained using UADFV, FaceForensics++, Celeb-DF, and DFDC datasets, and it was used to detect face-swapped deepfake videos created by frameworks such as DeepFaceLab and FakeApp. Experimental results show that IXFFVD outperforms the compared methods. Although the detection efficacy of this method could be further enhanced, future work will focus on incorporating spatiotemporal detection criteria to improve the detection of deepfake videos created from different datasets.