Deepfake Detection Method Based on Spatio-Temporal Information Fusion  

作　　者：Xinyi Wang Wanru Song Chuanyan Hao Feng Liu 

机构地区：[1]Department of Digital Media Technology,Nanjing University of Posts and Telecommunications,Nanjing,210023,China

出　　处：《Computers, Materials & Continua》2025年第5期3351-3368,共18页计算机、材料和连续体(英文)

基　　金：supported by National Natural Science Foundation of China(Nos.62477026,62177029,61807020);Humanities and Social Sciences Research Program of the Ministry of Education of China(No.23YJAZH047);the Startup Foundation for Introducing Talent of Nanjing University of Posts and Communications under Grant NY222034.

摘　　要：As Deepfake technology continues to evolve,the distinction between real and fake content becomes increasingly blurred.Most existing Deepfake video detectionmethods rely on single-frame facial image features,which limits their ability to capture temporal differences between frames.Current methods also exhibit limited generalization capabilities,struggling to detect content generated by unknown forgery algorithms.Moreover,the diversity and complexity of forgery techniques introduced by Artificial Intelligence Generated Content(AIGC)present significant challenges for traditional detection frameworks,whichmust balance high detection accuracy with robust performance.To address these challenges,we propose a novel Deepfake detection framework that combines a two-stream convolutional network with a Vision Transformer(ViT)module to enhance spatio-temporal feature representation.The ViT model extracts spatial features from the forged video,while the 3D convolutional network captures temporal features.The 3D convolution enables cross-frame feature extraction,allowing the model to detect subtle facial changes between frames.The confidence scores from both the ViT and 3D convolution submodels are fused at the decision layer,enabling themodel to effectively handle unknown forgery techniques.Focusing on Deepfake videos and GAN-generated images,the proposed approach is evaluated on two widely used public face forgery datasets.Compared to existing state-of-theartmethods,it achieves higher detection accuracy and better generalization performance,offering a robust solution for deepfake detection in real-world scenarios.

关 键 词：Deepfake detection vision transformer spatio-temporal information 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]