用全同态加密实现端到端人脸识别，全程保护隐私
CryptoFace: End-to-End Encrypted Face Recognition
- 采用全同态加密技术，全程保护人脸特征提取与匹配
- 在标准数据集上推理速度更快，识别准确率更高
- 适合对隐私安全要求高的生物识别应用场景
人脸识别广泛应用于身份认证、安防与个性化服务，但存在敏感生物特征数据被非法访问的重大隐私风险。本文提出CryptoFace，首个基于全同态加密（FHE）的端到端加密人脸识别系统，可在特征提取、存储和匹配全阶段安全处理人脸数据，无需暴露原始图像或特征。通过引入浅层块卷积网络，采用分块处理支持高维张量，降低乘法深度，从而减少推理延迟。并行化FHE评估使延迟接近分辨率无关。在标准人脸识别基准测试中，CryptoFace显著提升推理速度与验证准确率，优于现有适配人脸识别的FHE神经网络。代码已开源：https://github.com/human-analysis/CryptoFace。
原文摘要 · Abstract (English)
Face recognition is central to many authentication, security, and personalized applications. Yet, it suffers from significant privacy risks, particularly arising from unauthorized access to sensitive biometric data. This paper introduces CryptoFace, the first end-to-end encrypted face recognition system with fully homomorphic encryption (FHE). It enables secure processing of facial data across all stages of a face-recognition process--feature extraction, storage, and matching--without exposing raw images or features. We introduce a mixture of shallow patch convolutional networks to support higher-dimensional tensors via patch-based processing while reducing the multiplicative depth and, thus, inference latency. Parallel FHE evaluation of these networks ensures near-resolution-independent latency. On standard face recognition benchmarks, CryptoFace significantly accelerates inference and increases verification accuracy compared to the state-of-the-art FHE neural networks adapted for face recognition. CryptoFace will facilitate secure face recognition systems requiring robust and provable security. The code is available at https://github.com/human-analysis/CryptoFace.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。