Cross-database attack of different coding-based palmprint templates

Cross-database attack of different coding-based palmprint templates
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不同编码掌纹模板的跨数据库攻击

DOI:
10.1016/j.knosys.2023.110310
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发表时间:
2023-01
影响因子:
8.8
通讯作者:
Yi Zhang
Yi Zhang
中科院分区:
计算机科学1区
文献类型:
--
作者:
Ziyuan Yang;Lu Leng;Andrew Beng Jin Teoh;Bob Zhang;Yi Zhang

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生物识别系统中的跨数据库攻击是一种安全攻击,攻击者试图利用一个数据库中受损的目标用户模板,并在其他数据库中推断他们的模板。尽管跨数据库攻击对生物识别系统的安全构成了潜在的严重威胁,但生物识别界在很大程度上忽视了跨数据库攻击。本文对掌纹识别系统中的跨数据库攻击进行了全面的研究。我们特别关注基于编码的掌纹模板,因为它们很受欢迎。基于编码的掌纹特征表示方法设计不同,以提高性能准确性和降低复杂性,其中编码模板看起来完全不同;因此,很难以一种有意义的方式将它们联系起来。然而,我们证明了基于编码的掌纹模板的潜在相关性确实可以建立。具体来说,我们分析了六种基于编码的掌纹表示,并利用它们之间潜在的统计相关性,设计了一种有效的跨数据库攻击算法。该攻击可以利用目标用户的掌纹模板来推断存储在其他数据库中的模板,尽管编码方法不同。我们的跨数据库攻击甚至在公共数据集的某些场景中产生100%的成功率。这表明采用基于编码的表示的掌纹识别系统存在跨数据库攻击和侵犯隐私的高风险。
A cross-database attack in biometric systems is a security attack where attackers attempt to leverage a compromised target user’s template in one database and infer their templates in other databases. The biometric community largely ignores cross-database attack although they pose poses potential severe risks to the security of the biometric systems. This paper presents a comprehensive study on cross-database attacks in palmprint recognition systems. We specifically focus on the coding-based palmprint templates due to their popularity. Coding-based methods for palmprint feature representation are designed differently to improve performance accuracy and reduce complexity, where the coded templates look completely diverse; thus, it is difficult to correlate them in a meaningful way. However, we demonstrate that the latent correlation of coding-based palmprint templates can indeed be established. Specifically, we analyze six coding-based palmprint representations, and by exploiting the latent statistical correlations among them, we devise an effective cross-database attack algorithm. The attack enables the target users’ palmprint templates to be exploited to infer their templates stored in other databases despite different coding methods. Our cross-database attack even yields a 100% success rate in some scenarios on the public datasets. This suggests high risks of cross-database attacks and privacy invasion of palmprint recognition systems that adopt coding-based representation.
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