Abstract

Iris biometric is a widely deployed tool for biometric based user authentication. Its success has paved path for several security related attacks on this biometric. In this paper, we propose a deep learning perspective to solve two of these attacks—replay attack and database attack simultaneously. The proposed architecture HsIrisNet, a convolutional neural network (CNN), is trained and tested on two publicly available databases Casia-Iris-Interval v4 DB and IIT Delhi DB. The proposed approach HsIrisNet uses histograms of the selective robust regions followed by image upsampling to authenticate the user. These robust regions have been found to be sufficient enough to authenticate the user, enabling non-deterministic approach. The use of these selective regions for authentication enables the system to mitigate replay attack. While, the CNN model does not require saving any biometric data to the database, which mitigates database attack from the system. The comparison of proposed approach with existing state-of-art techniques show better performance of the proposed approach.

中文翻译：

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HsIrisNet：基于直方图的虹膜识别，可使用深度学习视角缓解重放和模板攻击

摘要

虹膜生物识别技术是用于基于生物识别的用户身份验证的广泛部署的工具。它的成功为该生物识别技术的多种安全相关攻击铺平了道路。在本文中，我们提出了深度学习的观点，以同时解决这两种攻击-重播攻击和数据库攻击。拟议的体系结构HsIrisNet，一个卷积神经网络（CNN），在两个公共数据库Casia-Iris-Interval v4 DB和IIT Delhi DB上进行了培训和测试。所提出的方法HsIrisNet使用选择性健壮区域的直方图，然后进行图像上采样来对用户进行身份验证。已经发现这些健壮的区域足以验证用户身份，从而可以采用非确定性方法。使用这些选择性区域进行身份验证可使系统减轻重放攻击。而，CNN模型不需要将任何生物识别数据保存到数据库，从而减轻了来自系统的数据库攻击。所提出的方法与现有技术水平的比较显示出所提出的方法的更好的性能。