计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (2): 323-335.doi: 10.11896/jsjkx.240200015

• 信息安全 • 上一篇    下一篇

基于深度学习的人脸呈现攻击检测方法研究进展

孙锐, 王菲, 冯惠东, 张旭东, 高隽   

  1. 合肥工业大学计算机与信息学院 合肥 230601
    合肥工业大学工业安全与应急技术安徽省重点实验室 合肥 230009
  • 收稿日期:2024-02-02 修回日期:2024-06-28 出版日期:2025-02-15 发布日期:2025-02-17
  • 通讯作者: 孙锐(sunrui@hfut.edu.cn)
  • 基金资助:
    国家自然科学基金面上项目(61876057);安徽省自然科学基金(2208085MF158);安徽省重点研发计划-科技强警专项(202004D07020012)

Research Progress in Facial Presentation Attack Detection Methods Based on Deep Learning

SUN Rui, WANG Fei, FENG Huidong, ZHANG Xudong, GAO Jun   

  1. School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230601,China
    Anhui Key Laboratory of Industry Safety and Emergency Technology,Hefei University of Technology,Hefei 230009,China
  • Received:2024-02-02 Revised:2024-06-28 Online:2025-02-15 Published:2025-02-17
  • About author:SUN Rui,born in 1976,Ph.D,professor.His main research interests include computer vision and machine learning.
  • Supported by:
    General Project of National Natural Science Foundation of China(61876057),National Natural Science Foundation of Anhui Province,China(2208085MF158) and Key Research and Development Plan of Anhui Province-Special Project for Strengthening Police with Science and Technology(202004D07020012).

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摘要： 随着人脸识别技术广泛应用于公共安全、金融支付等领域,呈现攻击(Presentation Attacks,PAs)对人脸识别系统的安全性构成了威胁。呈现攻击检测技术(Presentation Attacks Detection,PAD)旨在判断输入人脸的真伪,对维护识别系统的安全性和鲁棒性具有重要的研究意义。由于大规模数据集的不断涌现,基于深度学习的呈现攻击检测方法逐渐成为该领域的主流。文章对近期基于深度学习的人脸呈现攻击检测方法进行了综述。首先,概述了呈现攻击检测的定义、实施方式和常见的攻击类型;其次,分别从单模态和多模态入手,对近五年来深度学习类方法的发展趋势、技术原理和优缺点进行详细分析和总结;然后,介绍了PAD研究中使用的典型数据集及其特点,并给出算法的评估标准、协议和性能结果;最后,总结了PAD研究中面临的主要问题并展望了未来的发展趋势。

关键词: 呈现攻击检测, 单模态, 多模态, 人脸呈现数据集, 深度学习

Abstract: With the widespread application of facial recognition technology in fields such as public security and financial payments,presentation attacks(PAs) pose a serious challenge to the security of facial recognition systems.Presentation attacks detection(PAD) technology aims to determine the authenticity of input faces and has important research significance for maintaining the security and robustness of recognition systems.Due to the continuous emergence of large-scale datasets in recent years,deep learning-based PAD methods have gradually become the mainstream in this field.This paper offers a survey of current face PAD techniques based on deep learning.Firstly,it provides an overview of the definition,implementation methods,and common types of attack for PAD.Secondly,from the perspectives of single modality and multimodality,a thorough study is performed on the development trends,technical principles,benefits,and drawbacks of deep learning methods in the field of PAD over the previous five years.Thirdly,the common datasets and their characteristics that are used in PAD research are presented,and the evaluation standards,protocols,and algorithm performance results are given.Finally,we summarize the main issues faced in current PAD research and look forward to future development trends.

Key words: Presentation attack detection, Single modal, Multi-modal, Facial presentation dataset, Deep learning

中图分类号: 

  • TP391
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