Abstract
Modern face recognition systems utilize deep neural networks to extract salient features from a face. These features denote embeddings in latent space and are often stored as templates in a face recognition system. These embeddings are susceptible to data leakage and, in some cases, can even be used to reconstruct the original face image. To prevent compromising identities, template protection schemes are commonly employed. However, these schemes may still not prevent the leakage of soft biometric information such as age, gender and race. To alleviate this issue, we propose a novel technique that combines Fully Homomorphic Encryption (FHE) with an existing template protection scheme known as PolyProtect. We show that the embeddings can be compressed and encrypted using FHE and transformed into a secure PolyProtect template using polynomial transformation, for additional protection. We demonstrate the efficacy of the proposed approach through extensive experiments on multiple datasets. Our proposed approach ensures irreversibility and unlinkability, effectively preventing the leakage of soft biometric attributes from face embeddings without compromising recognition accuracy.
Abstract (translated)
现代面部识别系统利用深度神经网络从面部中提取显著特征。这些特征表示潜在空间中的嵌入，通常被面部识别系统中的模板存储。这些嵌入很容易受到数据泄漏的影响，在某些情况下，甚至可以用于重构原始面部图像。为了防止泄露身份，通常采用模板保护方案。然而，这些方案可能仍无法防止软生物特征（如年龄、性别和种族）的泄露。为了缓解这个问题，我们提出了一种结合完全同态加密（FHE）和已知模板保护方案（PolyProtect）的新技术。我们证明了使用FHE可以压缩和加密嵌入，并且可以使用多项式变换将其转换为安全的PolyProtect模板，提供额外的保护。我们通过在多个数据集上进行广泛实验，证明了所提出方法的有效性。与我们的方法相比，确保不可逆性和解链性，有效防止了未经过授权的软生物特征从面部嵌入中泄露，同时保持识别准确性。
URL
https://arxiv.org/abs/2404.16255
