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虹膜呈现攻击检测综述

王财勇 刘星雨 房美玲 赵光哲 何召锋 孙哲南

王财勇, 刘星雨, 房美玲, 赵光哲, 何召锋, 孙哲南. 虹膜呈现攻击检测综述. 自动化学报, 2024, 50(2): 241−281 doi: 10.16383/j.aas.c230109
引用本文: 王财勇, 刘星雨, 房美玲, 赵光哲, 何召锋, 孙哲南. 虹膜呈现攻击检测综述. 自动化学报, 2024, 50(2): 241−281 doi: 10.16383/j.aas.c230109
Wang Cai-Yong, Liu Xing-Yu, Fang Mei-Ling, Zhao Guang-Zhe, He Zhao-Feng, Sun Zhe-Nan. A survey on iris presentation attack detection. Acta Automatica Sinica, 2024, 50(2): 241−281 doi: 10.16383/j.aas.c230109
Citation: Wang Cai-Yong, Liu Xing-Yu, Fang Mei-Ling, Zhao Guang-Zhe, He Zhao-Feng, Sun Zhe-Nan. A survey on iris presentation attack detection. Acta Automatica Sinica, 2024, 50(2): 241−281 doi: 10.16383/j.aas.c230109

虹膜呈现攻击检测综述

doi: 10.16383/j.aas.c230109
基金项目: 国家自然科学基金(62106015, 62176025, 62276263), 北京市自然科学基金(4242018), 北京市科技新星计划(20230484444), 北京市科协青年人才托举工程(BYESS2023130), 北京建筑大学“建大英才”培养工程(JDYC20220819)资助
详细信息
    作者简介:

    王财勇:北京建筑大学电气与信息工程学院讲师. 2020年获得中国科学院自动化研究所博士学位. 主要研究方向为生物特征识别, 计算机视觉与模式识别. E-mail: wangcaiyong@bucea.edu.cn

    刘星雨:北京建筑大学电气与信息工程学院硕士研究生. 2020年获得浙江师范大学学士学位. 主要研究方向为生物特征识别. E-mail: liuxingyu@stu.bucea.edu.cn

    房美玲:德国达姆施塔特弗劳恩霍夫计算机图形研究所研究员. 2023年获得德国达姆施塔特工业大学博士学位. 主要研究方向为机器学习, 计算机视觉, 生物特征识别. E-mail: meiling.fang@igd.fraunhofer.de

    赵光哲:北京建筑大学电气与信息工程学院教授. 2012年获得日本名古屋大学博士学位. 主要研究方向为计算机视觉与图像处理, 模式识别, 人工智能. E-mail: zhaoguangzhe@bucea.edu.cn

    何召锋:北京邮电大学人工智能学院教授. 2010年获得中国科学院自动化研究所博士学位. 主要研究方向为生物特征识别, 视觉计算, 智能博弈决策, AI+IC协同优化. E-mail: zhaofenghe@bupt.edu.cn

    孙哲南:中国科学院自动化研究所研究员, 中国科学院大学人工智能学院教授. 2006年获得中国科学院自动化研究所博士学位. 主要研究方向为生物特征识别, 模式识别, 计算机视觉. 本文通信作者. E-mail: znsun@nlpr.ia.ac.cn

  • 中图分类号: Y

A Survey on Iris Presentation Attack Detection

Funds: Supported by National Natural Science Foundation of China (62106015, 62176025, 62276263), Beijing Natural Science Foundation (4242018), Beijing Nova Program (20230484444), Young Elite Scientist Sponsorship Program by BAST (BYESS2023130), and Pyramid Talent Training Project of BUCEA (JDYC20220819)
More Information
    Author Bio:

    WANG Cai-Yong Lecturer at School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture. He received his Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences in 2020. His research interest covers biometrics, computer vision, and pattern recognition

    LIU Xing-Yu Master student at School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture. She received her bachelor degree from Zhejiang Normal University in 2020. Her main research interest is biometrics

    FANG Mei-Ling Researcher at Fraunhofer Institute for Computer Graphics Research IGD, Darmstadt, Germany. She received her Ph.D. degree from Technical University of Darmstadt, Germany in 2023. Her research interest covers machine learning, computer vision, and biometrics

    ZHAO Guang-Zhe Professor at School of Electrical and Information Engineering, Beijing University of Civil Engineering and Architecture. He received his Ph.D. degree from Nagoya University, Japan in 2012. His research interest covers computer vision, image processing, pattern recognition, and artificial intelligence

    HE Zhao-Feng Professor at School of Artificial Intelligence, Beijing University of Posts and Telecommunications. He received his Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences in 2010. His research interest covers biometrics, visual computing, intelligent game decision-making, and AI+IC collaborative optimization

    SUN Zhe-Nan Professor at Institute of Automation, Chinese Academy of Sciences, and also at the School of Artificial Intelligence, University of Chinese Academy of Sciences. He received his Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences in 2006. His research interest covers biometrics, pattern recognition, and computer vision. Corresponding author of this paper

  • 摘要: 虹膜识别技术因唯一性、稳定性、非接触性、准确性等特性广泛应用于各类现实场景中. 然而, 现有的许多虹膜识别系统在认证过程中仍然容易遭受各种攻击的干扰, 导致安全性方面可能存在风险隐患. 在不同的攻击类型中, 呈现攻击(Presentation attacks, PAs)由于出现在早期的虹膜图像获取阶段, 且形式变化多端, 因而虹膜呈现攻击检测(Iris presentation attack detection, IPAD)成为虹膜识别技术中首先需要解决的安全问题之一, 得到了学术界和产业界的广泛重视. 本综述是目前已知第一篇虹膜呈现攻击检测领域的中文综述, 旨在帮助研究人员快速、全面地了解该领域的相关知识以及发展动态. 总体来说, 本文对虹膜呈现攻击检测的难点、术语和攻击类型、主流方法、公共数据集、比赛及可解释性等方面进行全面归纳. 具体而言, 首先介绍虹膜呈现攻击检测的背景、虹膜识别系统现存的安全漏洞与呈现攻击的目的. 其次, 按照是否使用额外硬件设备将检测方法分为基于硬件与基于软件的方法两大类, 并在基于软件的方法中按照特征提取的方式作出进一步归纳和分析. 此外, 还整理了开源方法、可申请的公开数据集以及概括了历届相关比赛. 最后, 对虹膜呈现攻击检测未来可能的发展方向进行了展望.
    1)  11下载地址: http://www.cripac.ia.ac.cn/people/znsun/irisclassification/CASIA-Iris-Fake.rar
  • 图  1  使用义眼进行虹膜呈现攻击图示(插图取自电影《辛普森一家》)

    Fig.  1  An illustration of iris presentation attack using artificial eye (the figure is from 《The Simpsons》)

    图  2  虹膜识别及虹膜呈现攻击检测的应用场景

    Fig.  2  Application scenarios of iris recognition and iris presentation attack detection

    图  3  具有虹膜呈现攻击检测功能的虹膜识别产品

    Fig.  3  Iris recognition products with IPAD function

    图  4  虹膜呈现攻击检测的中国专利数量

    Fig.  4  The number of Chinese patents related to IPAD

    图  5  申请虹膜呈现攻击检测中国专利的公司名称词云

    Fig.  5  Word cloud of companies applying for Chinese patents related to IPAD

    图  6  虹膜识别一般流程及关于呈现攻击的脆弱性

    Fig.  6  General pipeline of iris recognition and its vulnerability to presentation attacks

    图  7  虹膜呈现攻击检测和虹膜识别的两种集成方式

    Fig.  7  Tow schemes for integrating iris presentation attack detection and iris recognition

    图  8  真实虹膜与常见虹膜呈现攻击类型(绿色框内为真实样本, 红色框内为假体样本)

    Fig.  8  Bona fide iris and common iris presentationattack types (green box contains bona fide samples,while red box contains fake samples)

    图  9  虹膜呈现攻击类型分类(蓝色框内为使用真实虹膜的攻击, 绿色框内为使用人工制品的攻击,紫色框内表示合成虹膜攻击)

    Fig.  9  Taxonomy of iris presentation attack types (blue box indicates PAs using real iris, green box indicates PAs using artifacts, and purple box indicates PAs using synthetic iris)

    图  10  虹膜识别与屏显虹膜进行静态虹膜呈现攻击 (插图取自电影《坏蛋联盟》)

    Fig.  10  Iris recognition and static iris presentation attack using the iris displayed on the mobile phone (the figure is from 《The Bad Guys》)

    图  11  来自CASIA-Iris-Syn[44]中012子集的合成虹膜样例, 其中(b)为(a)的虹膜旋转所得, (c)为(a)的瞳孔收缩所得, (d)为(a)的虹膜离焦变换所得

    Fig.  11  Synthetic iris samples from the 012 subset of CASIA-Iris-Syn[44], where (b), (c) and (d) are obtained from the iris rotation, pupil constriction, and iris defocus transformation of (a), respectively

    图  12  真实虹膜与iDCGAN生成的虹膜[38]

    Fig.  12  Bona fide iris and iris generated by iDCGAN[38]

    图  13  虹膜呈现攻击检测的发展进程

    Fig.  13  Development and progression of IPAD

    图  14  虹膜呈现攻击检测的论文数量(数据来源:Web of Science, EI Compendex, 中国知网)

    Fig.  14  Number of papers on IPAD (Data source: Web of Science, EI Compendex, CNKI)

    图  15  不同波长下的多光谱虹膜图像[53]

    Fig.  15  The multi-spectral iris images atdifferent wavelengths[53]

    图  16  使用文献[55]的成像系统捕获的真实虹膜和伪造虹膜的样例图像

    Fig.  16  Example images of bona fide and fake irises by using the proposed camera system in [55]

    图  17  使用(a) OCT, (b) 近红外和(c) 可见光成像获取的真实活体虹膜、义眼和纹理隐形眼镜的样例图像, 其中可见光图像中的红线表示OCT扫描仪的遍历扫描方向[59]

    Fig.  17  Example images of bona fide iris, artificial eye and textured contact lens captured using (a) OCT, (b) NIR and (c) VIS imaging modalities, where the red line in the VIS image shows the traverse scanning direction of the OCT scanner[59]

    图  18  活体人眼在光照刺激下的瞳孔缩放效应示例

    Fig.  18  Illustration of the pupil contraction/dilation of live eye due to visible light stimulus

    图  19  近年来有代表性的基于软件的虹膜呈现攻击检测方法

    Fig.  19  Recent representative software-based iris presentation attack detection solutions

    图  20  GLCM计算过程示例

    Fig.  20  Example of GLCM calculation process

    图  21  纹理隐形眼镜图像的虹膜预处理过程[73]

    Fig.  21  Iris preprocessing process for images withtextured contact lens[73]

    图  22  基于质量相关特征的虹膜活体检测方法流程图[89]

    Fig.  22  General diagram of the iris liveness detection method based on quality related features[89]

    图  23  25种图像质量评价指标的分类[90]

    Fig.  23  Classification of the 25 image quality measures[90]

    图  24  不同的图像预处理模块, 其中(a)来自文献[96], (b)来自文献[97], (c)来自文献[98]

    Fig.  24  Different image preprocessing modules, where (a) is from [96], (b) is from [97], and (c) is from [98]

    图  25  基于微条纹分析的虹膜呈现攻击检测方法[100]

    Fig.  25  Micro stripes analyses for iris presentation attack detection[100]

    图  26  基于二分类(上)和单分类(下)的虹膜呈现攻击检测算法在处理未知攻击时的效果示意图[108]

    Fig.  26  Illustration of the effects of IPAD algorithms based on binary classification (top) and one-class classification (bottom) in handling unseen presentation attacks[108]

    图  27  D-NetPAD的特征可视化[20]

    Fig.  27  Feature visualization of D-NetPAD[20]

    图  28  AG-PAD的Grad-CAM热图[21]

    Fig.  28  Grad-CAM heatmaps of AG-PAD[21]

    图  29  不同方法的Score-CAM热图[9]

    Fig.  29  Score-CAM heatmaps of different methods[9]

    图  30  DCNN的Grad-CAM热图[22]

    Fig.  30  Grad-CAM heatmaps of DCNN[22]

    表  1  国内外虹膜识别主要厂商部署虹膜呈现攻击检测技术概览

    Table  1  Overview of IPAD technology deployed by major iris recognition manufacturers at home and abroad

    公司名称官方网址是否拥有
    虹膜呈现
    攻击检测
    技术
    技术支持
    方法支持检测的呈现攻击类型
    北京万里红科技有限公司http://www.superred.com.cn/卷积神经网络、视频序列分析美瞳、义眼、打印、
    屏显或重放攻击
    北京中科虹霸科技有限公司http://www.irisking.com/频谱分析、多尺度LBP、
    SIFT、CNN
    美瞳、义眼、打印或重放攻击
    上海点与面智能科技有限公司https://www.pixsur.com.cn/深度神经网络美瞳、义眼、打印、
    屏显或重放攻击
    上海聚虹光电科技有限公司http://www.irisian.com/红外灯闪烁、多光谱成像、
    机器学习
    美瞳或打印攻击
    北京眼神科技有限公司https://www.eyecool.cn/CNN、瞳孔光照反应美瞳、打印或重放攻击
    武汉虹识技术有限公司https://www.homsh.cn/LBP、GLCM、红外检测、
    深度学习
    美瞳、打印或合成攻击
    IriTech, Inc. (美国)https://iritech.com/虹膜动态变化N/R
    Iris ID (韩国)https://www.irisid.com/N/RN/R
    BioEnable Technologies (印度)https://www.bioenabletech.com/N/RN/R
    IrisGuard (英国)https://www.irisguard.com/瞳孔收缩变化、视频序列分析重放攻击
    EyeLock (美国)https://www.eyelock.com/多帧图像(视频)特征分析重放攻击
    Neurotechnology (立陶宛)https://www.neurotechnology.com/N/R美瞳或打印攻击
    注: N/R = not reported, 未公布.
    数据来源: 官网、问卷调查、专利.
    下载: 导出CSV

    表  2  虹膜呈现攻击检测方法汇总

    Table  2  Summary of iris presentation attack detection algorithms

    方法分类代表文献算法思想优点缺点
    一级分类二级分类
    基于硬件
    的方法
    多光谱成像[4954]眼组织不同层的光谱特性采集信息丰富, 检测
    准确率高, 可解释性好
    需要额外的成像设备, 成本较高, 采集效率低, 可能对用户有较大干扰
    3D成像[5559]眼睛的曲率、3D特性和
    内部结构
    瞳孔光照反应[6061]照明变化对瞳孔
    大小的影响
    眼动信号[6365]眼球运动过程中
    的物理特征
    基于软件
    的方法
    基于传统计算机视觉的方法基于图像纹理的方法[7376]LBP、BSIF、小波变换、
    GLCM等算子从灰度
    图中提取纹理特征
    不需要额外
    设备, 成本
    较低, 对用
    户的干扰
    较小
    计算复杂度低、容易实现, 适合纹理隐形眼镜检测泛化性不足
    基于图像质量的方法[8990]真假虹膜图像之间的
    “质量差异”
    简洁、快速、非接触性、用户友好、廉价容易误检真实噪声虹膜
    图像、未定制图像
    质量评价标准
    基于深度学习的方法传统CNNs[20, 48, 96103]通过CNNs进行虹膜
    真假分类
    特征提取和分类器学习联合优化、
    准确率较高
    计算复杂度高、容易
    过拟合、可解释
    性差
    生成对抗网络[108, 110111]生成器和判别器对抗博弈有利于检测未知攻击模型训练较为
    复杂、困难
    域自适应[10, 113]学习域不变特征提升检测泛化性收集目标域数据
    较困难
    注意力机制[9, 21, 119121]强化或者抑制特征映射提升CNN特征表达能力, 提高检测准确性和泛化性增加额外模型参数
    多源特征融合的方法[6, 122128]传统特征与深度特征融合、多模态特征融合融合特征的性能一般优于单一特征, 能提升检测的鲁棒性及泛化性计算复杂度高、
    部署困难
    下载: 导出CSV

    表  3  虹膜呈现攻击检测开源代码总览

    Table  3  Brief overview of open-source IPAD methods

    方法名称代码网址编程语言数据集
    PhotometricStereoIrisPAD[83]https://github.com/CVRL/PhotometricStereoIrisPADMATLABNDCLD15[86]
    OpenSourceIrisPAD[82]https://github.com/CVRL/OpenSourceIrisPADC++、PythonNDCLD15[86]、IIITD-WVU[14]
    LivDet-Iris 2017 (Clarkson)[14]
    RaspberryPiOpenSourceIris[85]https://github.com/CVRL/RaspberryPiOpenSourceIrisC++、PythonNDCLD15[86]、NDIris3D[84]
    Emvlc-ipad[124]https://github.com/akuehlka/emvlc-ipadPython、Objective-C、C++LivDet-Iris 2017[14]
    D-NetPAD[20]https://github.com/iPRoBe-lab/D-NetPADPythonNDCLD15[86]、LivDet-Iris 2017[14]
    AG-PAD[21]https://github.com/cunjian/AGPADPythonJHU-APL (私有)[21]、LivDet-Iris 2017[14]
    LFLD[58]https://github.com/luozhengquan/LFLDPythonCASIA-Iris-LFLD[57-58]
    下载: 导出CSV

    表  4  虹膜呈现攻击检测开放数据集总览

    Table  4  Brief overview of publicly available IPAD datasets

    数据集年份数据量(张)成像光谱攻击类型图像分辨率
    (像素)
    呈现攻击真实虹膜总数
    Warsaw-BioBase-Disease-Iris v1.0[36]2015441384825近红外、可见光病变$640\times480$
    Warsaw-BioBase-Disease-Iris v2.1[133]20152 2127842 996近红外、可见光病变$640\times480$
    Warsaw-BioBase-Post-Mortem-Iris v1.1[33]20161 59701 597近红外、可见光尸体$640\times480$
    Warsaw-BioBase-Post-Mortem-Iris v2.0[134]20192 98702 987近红外、可见光尸体$640\times480$
    Warsaw-BioBase-Post-Mortem-Iris v3.0[135]20201 87901 879近红外、可见光尸体$640\times480$
    CASIA-Iris-Syn[43]200810 000010 000N/A合成$640\times480$
    CASIA-Iris-Fake[136]20144 7306 00010 730近红外打印、隐形眼镜、
    义眼、合成
    大小不一
    CASIA-Iris-LFLD[5758]2019274230504近红外打印、屏显$128\times96$
    Eye Tracker Print-Attack Database (ETPAD v1)[63]2014200200400近红外打印$640\times480$
    Eye Tracker Print-Attack Database (ETPAD v2)[64]2015400400800近红外打印$640\times480$
    Synthetic Iris Textured Based[137]20067 00007 000N/A合成N/R
    Synthetic Iris Model Based[138]2007160 0000160 000N/A合成N/R
    UVCLI Database[139]20171 9251 8773 802可见光隐形眼镜N/R
    UnMIPA Database[93]20199 3879 31918 706近红外隐形眼镜N/R
    Cataract Mobile Periocular Database (CMPD)[140]2016N/RN/R2 380可见光病变$4 \;608\times3\; 456$
    WVU Mobile Iris Spoofing (IIITD-WVU) Dataset[14]20177 5072 95210 459近红外隐形眼镜、打印$640\times480$
    IIITD Contact Lens Iris Database[141]2013N/RN/R6 570近红外隐形眼镜$640\times480$
    ND Cosmetic Contact Lenses 2013 Dataset (NDCLD13)[142]20131 7003 4005 100近红外隐形眼镜$640\times480$
    The Notre Dame Contact Lense Dataset 2015 (NDCLD15)[86]20152 5004 8007 300近红外隐形眼镜$640\times480$
    The Notre Dame LivDet-Iris 2017 Subset[14]20172 4002 4004 800近红外隐形眼镜$640\times480$
    Notre Dame Photometric Stereo Iris Dataset (WACV 2019)[83]20192 6643 1325 796近红外隐形眼镜$640\times480$
    NDIris3D[84]20213 3923 4586 850近红外隐形眼镜$640\times480$
    注: N/R = not reported, 未公布.
    N/A = not applicable, 不适用.
    透明隐形眼镜虹膜图像归类为真实虹膜图像.
    下载: 导出CSV

    表  5  虹膜呈现攻击检测竞赛

    Table  5  Iris presentation attack detection competitions

    比赛名称组织者数据集图像数攻击类型成像光谱参赛团
    队数量
    冠军团队
    算法名称
    评价指标
    训练测试 BPCER (%)APCER (%)
    LivDet-Iris 2013[11]克拉克森大学670686打印、纹理隐形眼镜近红外3Federico28.565.72
    华沙工业大学4311 236
    圣母大学3 0001 200
    MoblLive 2014[12]INESC TEC800800打印可见光6IIT Indore0.500.00
    波尔图大学
    LivDet-Iris 2015[13]克拉克森大学(LG)1 8721 854打印、纹理隐形眼镜近红外4Federico1.685.48
    克拉克森大学(Dalsa)2 4191 836
    华沙工业大学1 6675 892
    LivDet-Iris 2017[14]克拉克森大学4 9373 158打印、纹理隐形眼镜近红外3Anon13.3614.71
    华沙工业大学4 5137 500
    圣母大学1 2002 700
    西弗吉尼亚大学
    印度理工学院德里分校6 2504 209
    LivDet-Iris 2020[15]克拉克森大学12 432打印、纹理隐形眼镜、屏显虹膜、义眼、尸体虹膜、组合攻击近红外3USACH/
    TOC
    0.4659.10
    华沙工业大学
    圣母大学
    瑞士IDIAP研究所
    华沙医科大学
    下载: 导出CSV
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  • 收稿日期:  2023-03-06
  • 录用日期:  2023-10-12
  • 网络出版日期:  2023-11-01
  • 刊出日期:  2024-02-26

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