论文标题
关于针对随机输入的生物特征验证系统的弹性
On the Resilience of Biometric Authentication Systems against Random Inputs
论文作者
论文摘要
我们对基于机器学习的生物识别身份验证系统的安全性针对提交统一的随机输入（作为特征向量或原始输入）的攻击者，以找到目标用户的接受样本。系统的平均误报率（FPR），即错误地接受冒犯的速率是合法用户，可以解释为衡量这种攻击成功概率的量度。但是，我们表明成功率通常高于FPR。特别是，对于一个平均FPR为0.03的重建生物识别系统，成功率高达0.78。这对系统的安全性有影响，作为攻击者，只有特征空间的长度知识可以平均少于2个尝试来模仿用户。我们提供详细的分析，分析为什么攻击成功，并使用四种不同的生物识别方式和四个不同的机器学习分类器来验证我们的结果。最后，我们提出的缓解技术使这种攻击无效，对系统的准确性几乎没有影响。
We assess the security of machine learning based biometric authentication systems against an attacker who submits uniform random inputs, either as feature vectors or raw inputs, in order to find an accepting sample of a target user. The average false positive rate (FPR) of the system, i.e., the rate at which an impostor is incorrectly accepted as the legitimate user, may be interpreted as a measure of the success probability of such an attack. However, we show that the success rate is often higher than the FPR. In particular, for one reconstructed biometric system with an average FPR of 0.03, the success rate was as high as 0.78. This has implications for the security of the system, as an attacker with only the knowledge of the length of the feature space can impersonate the user with less than 2 attempts on average. We provide detailed analysis of why the attack is successful, and validate our results using four different biometric modalities and four different machine learning classifiers. Finally, we propose mitigation techniques that render such attacks ineffective, with little to no effect on the accuracy of the system.
