安全多生物特征系统呈现攻击的统计元分析
Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems
摘要：
先前的工作已经表明，多生物特征系统容易受到呈现攻击，假设他们的匹配分数分布与真实用户的匹配分数分布相同，而不制造任何虚假特征。我们最近表明，这种假设并不代表当前的指纹和人脸呈现攻击，导致人们高估了多生物识别系统的脆弱性，并设计不太有效的融合规则。在本文中，我们通过提出一个面部和指纹呈现攻击的统计元模型来克服这些限制，该模型描述了更广泛的假分数分布，包括已知和潜在未知攻击的分布。这使我们能够对多生物识别系统进行全面的安全评估，以对抗演示攻击，量化它们的脆弱性如何在设计过程中考虑的攻击不同的情况下也会有所不同，通过不确定性分析。我们的经验表明，我们的方法可以可靠地预测多生物识别系统的性能，即使在以前从未见过的人脸和指纹呈现攻击，和使用我们的方法设计的安全融合规则可以表现出改进的性能之间的权衡在没有和存在的攻击。最后，我们认为，我们的方法可以扩展到其他生物特征，除了人脸和指纹。
英文摘要：
Prior work has shown that multibiometric systems are vulnerable to presentation attacks, assuming that their matching score distribution is identical to that of genuine users, without fabricating any fake trait. We have recently shown that this assumption is not representative of current fingerprint and face presentation attacks, leading one to overestimate the vulnerability of multibiometric systems, and to design less effective fusion rules. In this paper, we overcome these limitations by proposing a statistical meta-model of face and fingerprint presentation attacks that characterizes a wider family of fake score distributions, including distributions of known and, potentially, unknown attacks. This allows us to perform a thorough security evaluation of multibiometric systems against presentation attacks, quantifying how their vulnerability may vary also under attacks that are different from those considered during design, through an uncertainty analysis. We empirically show that our approach can reliably predict the performance of multibiometric systems even under never-before-seen face and fingerprint presentation attacks, and that the secure fusion rules designed using our approach can exhibit an improved trade-off between the performance in the absence and in the presence of attack. We finally argue that our method can be extended to other biometrics besides faces and fingerprints.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
