Biometric authentication systems have traditionally relied on the False Match Rate (FMR) to evaluate security against impersonation threats. However, this metric alone is insufficient for assessing vulnerabilities to statistical attacks because it cannot account for the non-uniformity of mismatches and atypical inputs that adversaries may manipulate. To address this issue, we propose a new evaluation framework that overcomes these limitations. The framework includes an estimate of the effective key space of biometrics and metrics that consider non-uniformity in the biometric embedding space. Our findings demonstrate that our framework provides a nuanced understanding of biometric security. Moreover, optimizing for the proposed metric leads to better security against statistical attacks than optimizing the FMR. Furthermore, the framework provides a comparative security analysis with traditional methods like passwords and PIN codes. It also quantifies the impact on security when adversaries partially know their victims, e.g., demographics.

中文翻译：

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生物识别安全的新颖评估框架：评估猜测难度作为指标


传统上，生物特征认证系统依靠错误匹配率 (FMR) 来评估针对假冒威胁的安全性。然而，仅此指标不足以评估统计攻击的漏洞，因为它无法解释对手可能操纵的不匹配和非典型输入的不均匀性。为了解决这个问题，我们提出了一个新的评估框架来克服这些限制。该框架包括对生物识别有效密钥空间的估计以及考虑生物识别嵌入空间中的不均匀性的指标。我们的研究结果表明，我们的框架提供了对生物识别安全性的细致入微的理解。此外，与优化 FMR 相比，针对所提出的指标进行优化可以更好地抵御统计攻击。此外，该框架还提供了与密码和 PIN 码等传统方法的比较安全分析。它还量化了当对手部分了解受害者（例如人口统计数据）时对安全的影响。