SecureDA: Privacy-Preserving Source-Free Domain Adaptation for Person Re-Identification
Xiaofeng Qu, Li Liu, Huaxiang Zhang, Lei Zhu, Liqiang Nie, Xiaojun Chang, Fengling Li · IEEE Transactions on Multimedia · 2025
Conventional domain adaptation (DA) for person re-identification (ReID) aims to bridge the domain gap but often requires direct use of fully labeled source and target domains, raising significant data privacy concerns due to the inclusion of personal identity information (PII) in raw data. Source-free domain adaptation (SFDA) for person ReID effectively preserves PII within the authorized source model. Nevertheless, these methods are vulnerable to data privacy (e.g., portrait rights) of the target domain during retrieval, where attackers can exploit pedestrian images for malicious generation, leading to damage to an individual’s reputation. Beyond these limitations, we propose a novel framework called SecureDA to address privacy-preserving SFDA for person ReID, which can generate a privacy key to defend against potential attacks on PII. Technically, we introduce domain-specific adversarial attacks into DA, where the protected query and gallery images are encrypted to ensure secure image retrieval. Furthermore, we employ two simultaneous processes: 1) The global–local adversarial pathway (GLAP) leverages encrypted and original images as adversarial pairs, thereby fostering the development of robust ReID models; 2) The global–local collaborative pathway (GLCP) is mastered through positive pairs collected from the same domain, effectively mitigating the pernicious catastrophic forgetting phenomenon. Extensive experiments show that SecureDA achieves state-of-the-art performance on multiple DA benchmarks and even outperforms the conventional DA and SFDA methods, which inherently compromise data privacy.