Powered by new advances in sensor development and artificial intelligence, the decreasing cost of computation, and the pervasiveness of handheld computation devices, biometric user authentication (and identification) is rapidly becoming ubiquitous. Modern approaches to biometric authentication, based on sophisticated machine learning techniques, cannot avoid storing either trained-classifier details or explicit user biometric data, thus exposing users' credentials to falsification. In this paper, we introduce a secure way to handle user-specific information involved with the use of vector-space classifiers or artificial neural networks for biometric authentication. Our proposed architecture, called a Neural Fuzzy Extractor (NFE), allows the coupling of pre-existing classifiers with fuzzy extractors, through a artificial-neural-network-based buffer called an expander, with minimal or no performance degradation. The NFE thus offers all the performance advantages of modern deep-learning-based classifiers, and all the security of standard fuzzy extractors. We demonstrate the NFE retrofit to a classic artificial neural network for a simple scenario of fingerprint-based user authentication.

翻译：随着传感器技术和人工智能的最新进展、计算成本的持续降低以及手持计算设备的普及，基于生物特征的用户认证（及识别）正迅速变得无处不在。基于先进机器学习技术的现代生物特征认证方法无法避免存储训练分类器细节或用户明确的生物特征数据，从而使用户凭证面临被篡改的风险。本文提出了一种安全的方式来处理在基于向量空间分类器或人工神经网络的生物特征认证中涉及的用户特有信息。我们所提出的架构称为神经模糊提取器（NFE），它通过一种名为扩展器的人工神经网络缓冲结构，能够将已有分类器与模糊提取器相结合，同时性能损失极小或为零。因此，NFE兼具现代深度学习分类器的全部性能优势与标准模糊提取器的全部安全性。我们通过一个基于指纹的用户认证简单场景，展示了将NFE改造应用于经典人工神经网络的实例。