Nowadays, biometric-based authentication is gaining immense popularity due to the widespread usage of digital activities. Among various biometric traits, the iris is one of the most discriminative, accurate, and popularly used biometrics. However, due to its immutable nature, it is highly vulnerable to adversarial attacks if stolen and thus poses a severe security threat. Here, in this work, we propose a cancelable iris biometric authentication system that stores a transformed version of the original iris template and thus enables cancelation and re-enrolment in case if the original template is stolen. Firstly, for extracting discriminative iris features, we have proposed a novel deep architecture based on aggregation learning. This deep architecture makes use of qualitative measure (ordinal measure), unlike popularly used quantitative measures. The usage of ordinal measures in this work enables to encode distinctive iris features quite well. Later generated iris features are protected using state-of-the-art two representative cancelable biometric techniques, namely BioHashing and 2^N discretized BioPhasor. Finally, in order to justify the efficacy of the proposed architecture, we have presented rigorous and holistic security analysis. To the best of our knowledge, this is the first work that has presented such an in-depth analysis of any deep network in the context of cancelable iris biometrics. Experimental results over four datasets viz. CASIA-V3 Interval, CASIA-Lamp, IITD, and IITK demonstrate the efficacy of the proposed framework in terms of security and accuracy. Further, for better network explainability, we have also performed layer-specific heatmap and feature map analysis to ascertain what exactly our novel deep architecture is learning.

如今，由于数字活动的广泛使用，基于生物特征的身份验证正变得越来越流行。在各种生物特征中，虹膜是最有区别，最准确和最常用的生物特征之一。但是，由于其不变性，如果被盗，它极易受到对抗性攻击，因此构成了严重的安全威胁。在这里，在这项工作中，我们提出了一种可取消的虹膜生物特征认证系统，该系统存储了原始虹膜模板的转换版本，因此可以在原始模板被盗的情况下取消并重新注册。首先，为了提取有区别的虹膜特征，我们提出了一种基于聚合学习的新型深度架构。这种深层结构使用定性度量（常规度量），与流行的定量度量不同。在这项工作中使用顺序测量可以很好地编码独特的虹膜特征。最新的虹膜特征使用最新的两种代表性可取消生物特征技术进行保护，即BioHashing和2^N个离散化的BioPhasor。最后，为了证明所提出的体系结构的有效性，我们提出了严格而全面的安全性分析。据我们所知，这是在可取消虹膜生物特征识别技术背景下对任何深层网络进行了如此深入分析的第一项工作。以上四个数据集实验结果即。CASIA-V3间隔，CASIA-Lamp，IITD和IITK在安全性和准确性方面证明了所提出框架的有效性。此外，为了获得更好的网络可解释性，我们还进行了特定于层的热图和特征图分析，以确定我们的新型深度架构到底在学习什么。