A Self-Semantic-Structural-Guided Approach for Training-Free Full-Hand De-Identification

Abstract

The human hand serves as a vital biometric modality, with the palm, fingers, and knuckles commonly employed for identity recognition across a range of applications. Despite the abundance of sensitive identity cues present in hand imagery, comprehensive privacy protection, particularly full-hand de-identification, remains underexplored. To address this gap, we introduce a self-semantic-structural-guided de-identification framework that operates directly on a single hand image, eliminating the need for training or optimization. Our method builds upon a pre-trained diffusion-based inpainting model, enhanced with IP-Adapter and ControlNet to incorporate semantic and structural cues. To mitigate identity leakage, we devise a selective semantic injection strategy and a depth-based structural de-identification strategy for each branch. Additionally, to maintain background integrity, we implement a self-adaptive hand-blending technique that seamlessly integrates the de-identified hand into the original scene. Extensive evaluations across multiple datasets and recognition systems validate the method’s effectiveness in preserving image quality, usability, and de-identification fidelity.