Research on video face forgery detection model based on multiple feature fusion network.

In recent years, the nefarious exploitation of video face forgery technology has emerged as a grave threat, not only to personal property security but also to the broader stability of states and societies. Although numerous models and methods have emerged for video face forgery detection, these methods fall short in recognizing subtle traces of forgery in local regions, and the performance of the detection models is often affected to some extent when dealing with specific forgery strategies. To solve this problem, we propose a model based on multiple feature fusion network (MFF-Net) for video face forgery detection. The model employs Res2Net50 to extract texture features of the video, which realizes deeper texture feature extraction. By integrating the extracted texture and frequency feature into a temporal feature extraction module, which includes a three-layer LSTM network, the detection model fully incorporates the diverse features of the video information, thus identifying the subtle artifacts more effectively. To further enhance the discrimination ability of the model, we have also introduced a texture activation module (TAM) in the texture feature extraction section. It helps to enhance the saliency of subtle forgery traces, thus improving the detection of specific forgery strategies. In order to verify the effectiveness of the proposed method, we conduct experiments on several generalized datasets such as FaceForensics++ and DFD. The experimental results demonstrate that the MFF-Net model can recognize subtle forgery traces more effectively, especially in the case of a particular forgery strategy, and the model exhibits excellent performance and high detection accuracy. [ABSTRACT FROM AUTHOR]