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Feature Selection on Handwriting Biometrics: Security Aspects of Artificial Forgeries

机译:手写生物特征的特征选择:人工伪造的安全性方面

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A lot of improvements were introduced lately in order to increase the verification performance of biometric user authentication systems. One method, besides many others, is the selection of specific features for each user during the verification process. In this paper we present a security analysis of a user specific bit mask vector, which was originally introduced to improve verification performance on a Biometric Hash algorithm for dynamic handwriting. Therefore, we use a reverse engineering attack method to generate artificial handwriting data and calculate error rates to examine the impact on the verification performance. Our goal is to study the effect of a feature selection by a mask vector on artificial data in comparison to genuine handwriting data. Our first experimental results show an average decrease of the equal error rate, generate by the artificial data, by approx. 64%. In comparison, equal error rates of random attacks, using verification data of another user, decreases by an average of approx. 27%.
机译:最近引入了许多改进,以提高生物识别用户身份验证系统的验证性能。除许多其他方法外,一种方法是在验证过程中为每个用户选择特定功能。在本文中,我们介绍了用户特定位掩码向量的安全性分析,该分析最初是为了提高针对动态手写的生物特征哈希算法的验证性能而引入的。因此,我们使用逆向工程攻击方法来生成人工手写数据并计算错误率,以检查对验证性能的影响。我们的目标是研究与真实笔迹数据相比,通过掩模矢量进行特征选择对人造数据的影响。我们的第一个实验结果表明,由人工数据产生的平均错误率平均降低了约。 64%。相比之下,使用另一位用户的验证数据,随机攻击的均等错误率平均降低了约5%。 27%。

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