Toward Real-World Holistic Privacy-Preserving Person Re-Identification

Real-world person re-identification (Re-ID) systems are susceptible to malicious attacks, leading to the leakage of pedestrian images and the Re-ID model, posing severe threats to the privacy of both system owners and pedestrians. Existing privacy-preserving person re-identification (PPPR) methods fail to simultaneously resist data leakage, model leakage, and data & model leakage while compromising the normal functionality of Re-ID systems. In this paper, we begin with an in-depth analysis of prior methodologies and identify the gap between existing works and the ideal PPPR paradigm. Inspired by the concept of "Let the invisible perturbation become the system trigger", we propose SHIELD, a pioneering and comprehensive two-stage privacy-preserving framework. To resist data leakage, we propose a self-supervised method for Protected Dataset Generation in the first stage, which obviates the dependence on identity labels and ensures image quality. To resist model leakage without compromising the normal retrieval accuracy, we propose Original Feature Deconstruction and Protected Feature Alignment to train the system model with paired protected and original images. Extensive experiments substantiate that SHIELD significantly outperforms existing PPPR methods, offering robust and holistic protection for Re-ID systems while maintaining decent retrieval accuracy for authorized users. The code will be released soon.