人脸深度伪造检测技术研究综述

doi:10.11896/jsjkx.251100118

Abstract

Abstract: With the rapid advancement of AI-generated content(AIGC),social media and other online platforms have been inundated with large volumes of high-quality face deepfake images and videos,which pose significant security threats to personal privacy protection and the social public opinion ecosystem.As a key means to address this issue,face deepfake detection technology has emerged as a research focus in recent years.This paper provides a comprehensive overview of existing technologies,extending from a data modality perspective to a learning paradigm perspective.Firstly,it systematically reviews the mainstream and representative image-level and video-level face forgery detection technologies.From the perspectives of feature dimensions and technical architectures,the former is categorized into methods based on the spatial domain,frequency domain,and spatial-frequency combination;the latter is categorized into methods based on biological features and spatio-temporal inconsistency.Secondly,since the collaborative relationships of multimodal information in real-world scenarios are difficult to be perfectly replicated,multimodal face deepfake detection technologies are further explored.Building on the analysis of audio forgery detection,the review focuses on methods based on cross-modal consistency and multi-modal feature fusion.Furthermore,the review shifts perspective to explore detection technologies oriented towards advanced learning paradigms.It highlights generalization and interpretability me-thods addressing cross-domain adaptability and decision transparency challenges,and analyzes the new paradigm of large model-aided detection.In addition,the mainstream face deepfake detection datasets and common evaluation metrics are summarized,thereby providing a reference for technical comparison and performance assessment.Finally,the limitations of the current face deepfake detection field are summarized,and prospects for future development directions are presented.

Key words: Deepfake detection, Spatial domain, Frequency domain, Spatiotemporal inconsistency, Multimodal collaboration

CLC Number:

TP391

WANG Lihua, WANG Xinyu, YAN Weidan, ZHANG Dengyin. Review of Research on Face Deepfake Detection Technology[J].Computer Science, 2026, 53(8): 375-387.

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