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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.Abstract: Iris recognition technology has been widely deployed in various real-world scenarios due to its advantages of uniqueness, stability, non-contact nature, accuracy and so on. However, many existing iris recognition systems are still vulnerable to various attacks during the authentication process, posing potential security risks. Among different types of attacks, presentation attacks (PAs) occur in the early stage of iris image acquisition and take various forms. Therefore, iris presentation attack detection (IPAD) has become one of the primary security issues that need to be addressed in iris recognition technology, attracting significant attention from academia and industry. To the best of our knowledge, this is the first Chinese review paper in the field of IPAD, aiming to help researchers quickly and comprehensively understand the relevant knowledge and development trends in this field. Overall, this paper provides a comprehensive summary of IPAD, covering broad topics on the challenges, terminology, PA types, mainstream methods, publicly available datasets, competitions, interpretability and so on. Specifically, we first introduce the background of IPAD, existing security vulnerabilities in iris recognition systems, and the objectives of PAs. Then, IPAD methods are categorized into two groups: Hardware-based and software-based methods, by considering the use of additional hardware devices. Further categorization and analysis are provided for software-based methods based on feature extraction manners. Additionally, we collate open-source methods, publicly available datasets, and summarize past relevant competitions. Finally, we discuss the future development direction of IPAD for further research.1)
1 1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 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 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 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
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/R N/R BioEnable Technologies (印度) https://www.bioenabletech.com/ √ N/R N/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.
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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 [49−54] 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 [55−59] 眼睛的曲率、3D特性和
内部结构瞳孔光照反应 [60−61] 照明变化对瞳孔
大小的影响眼动信号 [63−65] 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 [73−76] 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 [89−90] 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, 96−103] 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, 110−111] 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, 119−121] 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, 122−128] 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
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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/PhotometricStereoIrisPAD MATLAB NDCLD15[86] OpenSourceIrisPAD[82] https://github.com/CVRL/OpenSourceIrisPAD C++、Python NDCLD15[86]、IIITD-WVU[14]、
LivDet-Iris 2017 (Clarkson)[14]RaspberryPiOpenSourceIris[85] https://github.com/CVRL/RaspberryPiOpenSourceIris C++、Python NDCLD15[86]、NDIris3D[84] Emvlc-ipad[124] https://github.com/akuehlka/emvlc-ipad Python、Objective-C、C++ LivDet-Iris 2017[14] D-NetPAD[20] https://github.com/iPRoBe-lab/D-NetPAD Python NDCLD15[86]、LivDet-Iris 2017[14] AG-PAD[21] https://github.com/cunjian/AGPAD Python JHU-APL (私有)[21]、LivDet-Iris 2017[14] LFLD[58] https://github.com/luozhengquan/LFLD Python CASIA-Iris-LFLD[57-58]
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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] 2015 441 384 825 近红外、可见光 病变 $640\times480$ Warsaw-BioBase-Disease-Iris v2.1[133] 2015 2 212 784 2 996 近红外、可见光 病变 $640\times480$ Warsaw-BioBase-Post-Mortem-Iris v1.1[33] 2016 1 597 0 1 597 近红外、可见光 尸体 $640\times480$ Warsaw-BioBase-Post-Mortem-Iris v2.0[134] 2019 2 987 0 2 987 近红外、可见光 尸体 $640\times480$ Warsaw-BioBase-Post-Mortem-Iris v3.0[135] 2020 1 879 0 1 879 近红外、可见光 尸体 $640\times480$ CASIA-Iris-Syn[43] 2008 10 000 0 10 000 N/A 合成 $640\times480$ CASIA-Iris-Fake[136] 2014 4 730 6 000 10 730 近红外 打印、隐形眼镜、
义眼、合成大小不一 CASIA-Iris-LFLD[57−58] 2019 274 230 504 近红外 打印、屏显 $128\times96$ Eye Tracker Print-Attack Database (ETPAD v1)[63] 2014 200 200 400 近红外 打印 $640\times480$ Eye Tracker Print-Attack Database (ETPAD v2)[64] 2015 400 400 800 近红外 打印 $640\times480$ Synthetic Iris Textured Based[137] 2006 7 000 0 7 000 N/A 合成 N/R Synthetic Iris Model Based[138] 2007 160 000 0 160 000 N/A 合成 N/R UVCLI Database[139] 2017 1 925 1 877 3 802 可见光 隐形眼镜 N/R UnMIPA Database[93] 2019 9 387 9 319 18 706 近红外 隐形眼镜 N/R Cataract Mobile Periocular Database (CMPD)[140] 2016 N/R N/R 2 380 可见光 病变 $4 \;608\times3\; 456$ WVU Mobile Iris Spoofing (IIITD-WVU) Dataset[14] 2017 7 507 2 952 10 459 近红外 隐形眼镜、打印 $640\times480$ IIITD Contact Lens Iris Database[141] 2013 N/R N/R 6 570 近红外 隐形眼镜 $640\times480$ ND Cosmetic Contact Lenses 2013 Dataset (NDCLD13)[142] 2013 1 700 3 400 5 100 近红外 隐形眼镜 $640\times480$ The Notre Dame Contact Lense Dataset 2015 (NDCLD15)[86] 2015 2 500 4 800 7 300 近红外 隐形眼镜 $640\times480$ The Notre Dame LivDet-Iris 2017 Subset[14] 2017 2 400 2 400 4 800 近红外 隐形眼镜 $640\times480$ Notre Dame Photometric Stereo Iris Dataset (WACV 2019)[83] 2019 2 664 3 132 5 796 近红外 隐形眼镜 $640\times480$ NDIris3D[84] 2021 3 392 3 458 6 850 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.
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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 670 686 打印、纹理隐形眼镜 近红外 3 Federico 28.56 5.72 华沙工业大学 431 1 236 圣母大学 3 000 1 200 MoblLive 2014[12] INESC TEC 800 800 打印 可见光 6 IIT Indore 0.50 0.00 波尔图大学 LivDet-Iris 2015[13] 克拉克森大学(LG) 1 872 1 854 打印、纹理隐形眼镜 近红外 4 Federico 1.68 5.48 克拉克森大学(Dalsa) 2 419 1 836 华沙工业大学 1 667 5 892 LivDet-Iris 2017[14] 克拉克森大学 4 937 3 158 打印、纹理隐形眼镜 近红外 3 Anon1 3.36 14.71 华沙工业大学 4 513 7 500 圣母大学 1 200 2 700 西弗吉尼亚大学 — — 印度理工学院德里分校 6 250 4 209 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 3 USACH/
TOC0.46 59.10 华沙工业大学 圣母大学 瑞士IDIAP研究所 华沙医科大学
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