摘要
Abstract
Face liveness detection serves as a critical security barrier in identity verification systems.However,existing technologies are facing severe challenges due to the increasing prevalence of attacks such as video injection,video replay attacks,and AI-generated content(AIGC)attacks.This paper systematically reviews the technical principles of face liveness detection,categorizing them into software-based and hardware-assisted solutions,and provides a comparative analysis of mainstream approaches including action instructions,texture analysis,and multispectral imaging.Furthermore,it delves into the implementation mechanisms and defense challenges of video injection attacks,video replay attacks,and deepfake-based AIGC attacks.Based on this analysis,a multi-layered defense-in-depth strategy is proposed from four dimensions:interaction design optimization,hardware capability integration,environmental perception and risk control,and mobile vendor capability openness.Finally,the paper predicts that liveness detection will evolve towards passive detection,multimodal fusion,AI-versus-AI confrontation,and standardized openness.This study aims to provide systematic technical support and strategic recommendations for optimizing and upgrading face recognition security systems.关键词
人脸活体检测/深度伪造/视频回放攻击/多模态融合/环境感知Key words
Face Liveness Detection/Deepfake/Video Replay Attack/Multimodal Fusion/Environ-mental Perception分类
信息技术与安全科学
