Deepfake Detection Technology Integrating Spatial Domain and Frequency Domain
Haoqi Geng, Tianliang Lu, Wanxin Huang, Bowen Ding · Frontiers in Computing and Intelligent Systems · 2025
In recent years, with the rapid development of deep forgery technology, its verisimilitude is increasing day by day, and its social impact is becoming more and more serious. However, although a variety of face deep forgery video detection algorithms have been proposed and have shown certain detection capabilities on open source data sets, in the face of increasingly sophisticated deep forgery technology, the differences between genuine and fake videos are gradually difficult to be captured by the naked eye, and existing detection methods generally have problems such as low cross-compressibility detection and poor robustness. Therefore, in order to improve the detection accuracy and model robustness, a deep forged video detection method named SFDT is proposed. In this scheme, the framework structure of fusion of air frequency domain is adopted. Firstly, feature extraction is enhanced by improving MVIT in the airspace and using ASFF adaptive module; secondly, frequency domain features are extracted by dynamic filter in the frequency domain; then FECAM is used to reduce the loss caused by frequency domain information transmission; finally, multi-mode fusion module is used for feature fusion. This air-frequency fusion detection scheme can not only improve the cross-compression detection performance of the model, but also effectively deal with various interference in the transmission of video, and improve the robustness of the model.