用加密技术保护人脸数据，实现安全的身份验证。
A Machine Learning-Based Secure Face Verification Scheme and Its Applications to Digital Surveillance
- 在密文域中运行EM算法，结合DeepID2提取特征
- 三种隐私级别系统，支持社区安防场景部署
- 兼顾隐私与效率，适合对安全要求高的监控应用
人脸验证是当前社会广泛使用的一种图像分析技术，但大多数实际系统忽视了对敏感面部图像的保护。为解决此问题，本文提出一种基于机器学习的安全人脸验证方案：利用DeepID2卷积神经网络提取面部特征，并通过EM算法实现验证。为保障面部数据隐私，采用同态加密对数据进行加密，并在密文域中执行EM算法。本文设计了三种针对社区监控（或出入口控制）的隐私等级不同的验证系统，给出了相应的运行时性能，证明其具备实际部署可行性。
原文摘要 · Abstract (English)
Face verification is a well-known image analysis application and is widely used to recognize individuals in contemporary society. However, most real-world recognition systems ignore the importance of protecting the identity-sensitive facial images that are used for verification. To address this problem, we investigate how to implement a secure face verification system that protects the facial images from being imitated. In our work, we use the DeepID2 convolutional neural network to extract the features of a facial image and an EM algorithm to solve the facial verification problem. To maintain the privacy of facial images, we apply homomorphic encryption schemes to encrypt the facial data and compute the EM algorithm in the ciphertext domain. We develop three face verification systems for surveillance (or entrance) control of a local community based on three levels of privacy concerns. The associated timing performances are presented to demonstrate their feasibility for practical implementation.
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