Latent-RAG: Identity Retrieval-Guided Latent Augmentation for Privacy-Preserving Person Re-Identification

Abstract

Person re-identification (re-ID) is crucial for security applications, including autonomous robots that monitor individuals via continuous image acquisition. Such data are transmitted to a database; however, if stored without adequate protection, they can be intercepted, posing privacy risks. In response, the existing methods balance privacy and accuracy, but protected images still reveal structural cues, such as silhouettes or edges. These methods rely on randomness to defend against recovery attacks, limiting the guarantee of complete protection. Thus, this work proposes latent retrieval-augmented generation (RAG), an identity retrieval-guided latent augmentation framework for privacy-preserving person re-ID that balances the re-ID performance with privacy protection. The proposed method generates augmented codes that distort appearance and disrupt mapping to the original input by retrieving identity-similar latent codes and applying inverse self-attention, enhancing its robustness to recovery attacks. Next, this approach employs gradient-based latent code manipulation to preserve identity vectors to maintain re-ID accuracy. The hierarchical latent codes are concurrently adjusted to eliminate structural cues that could threaten privacy. The experimental results demonstrate that Latent-RAG induces strong visual distortion, reliable re-ID accuracy and a robust defense against recovery attacks, even without additional training with a few frozen parameters in a pretrained generator. Our code is available at https://github.com/BACKAI/Latent-RAG.