Deepfake Video Detection Method Based on Multi-scale Feature
YU Yang
SHEN Qun
ZHA Keke
CAI Jiyuan
Abstract：The development and malicious use of Deepfake videos pose a huge threat to personal,social,and national securi-ty.Therefore,research on Deepfake video detection technology is of great significance.At present,there are problems such as loss of video spatiotemporal information and single feature form in the feature extraction process of detection technology at this stage.This paper proposes a multi-scale feature fusion 3D convolutional network 3D MSFFCN.By improving the multi-scale convolutional attention network and multi-frequency feature fusion module the network feature extraction ability is improved.Finally,the pro-posed algorithm is trained and tested on the data set DFDC,and the results of the evaluation index accuracy and AUC value are 87.50%and 0.967.By comparing with other models and using the Captum tool to analyze the interpretability of the proposed meth-od,it is proved that the method proposed in this paper has a good effect.
Keywords：DeepfakeDeepfake detectionmulti-scale feature3D CNNsconvolutional attention
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：7( 3085-3091 )