用可撤销密钥混合生成人脸，实现高保真匿名与强隐私保护
FaceAnonyMixer: Cancelable Faces via Identity Consistent Latent Space Mixing
- 通过预训练模型潜空间混合真实脸图与可撤销合成码生成匿名人脸
- 在商用API上比现有方法提升超11%识别准确率，同时满足可撤销等隐私要求
- 无需修改现有识别系统，适合需高隐私保护的生物特征应用
人脸识别技术的进步加剧了隐私担忧，亟需在保持识别可用性的同时保护身份信息。现有匿名化方法多聚焦于模糊身份，难以满足生物模板保护所需的可撤销性、不可关联性和不可逆性。我们提出FaceAnonyMixer，一种基于预训练生成模型潜空间的可撤销人脸生成框架。其核心思想是将真实人脸的潜码与由可撤销密钥生成的合成潜码不可逆地混合，并通过多目标损失函数优化，以满足所有可撤销生物特征需求。该方法可直接生成高质量可撤销人脸，兼容现有面部识别系统，无需修改。在基准数据集上的大量实验表明，FaceAnonyMixer在保持更优识别精度的同时，显著增强隐私保护，在商业API上相比近期方法提升超过11%。代码已公开于：https://github.com/talha-alam/faceanonymixer。
原文摘要 · Abstract (English)
Advancements in face recognition (FR) technologies have amplified privacy concerns, necessitating methods that protect identity while maintaining recognition utility. Existing face anonymization methods typically focus on obscuring identity but fail to meet the requirements of biometric template protection, including revocability, unlinkability, and irreversibility. We propose FaceAnonyMixer, a cancelable face generation framework that leverages the latent space of a pre-trained generative model to synthesize privacy-preserving face images. The core idea of FaceAnonyMixer is to irreversibly mix the latent code of a real face image with a synthetic code derived from a revocable key. The mixed latent code is further refined through a carefully designed multi-objective loss to satisfy all cancelable biometric requirements. FaceAnonyMixer is capable of generating high-quality cancelable faces that can be directly matched using existing FR systems without requiring any modifications. Extensive experiments on benchmark datasets demonstrate that FaceAnonyMixer delivers superior recognition accuracy while providing significantly stronger privacy protection, achieving over an 11% gain on commercial API compared to recent cancelable biometric methods. Code is available at: https://github.com/talha-alam/faceanonymixer.
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