Face Privacy Protection and Anonymization Model Integrating Multi-Condition Collaborative Control GANs
TL;DR
A reversible face anonymization framework based on multi-conditional collaborative control (RFAMCC), integrating multi-level feature-driven anonymization, identity-space deflection, context fusion, and a steganography–recovery mechanism is proposed, providing a practical solution for privacy-preserving and legally compliant facial data sharing.
Abstract
The widespread deployment of face recognition and visual analytics has made balancing privacy protection and image usability a critical challenge. Existing anonymization methods often rely on blurring or occlusion, which suppress identity but distort key non-identity attributes. To address this issue, this study proposes a reversible face anonymization framework based on multi-conditional collaborative control (RFAMCC), integrating multi-level feature-driven anonymization, identity-space deflection, context fusion, and a steganography–recovery mechanism. Experimental evaluations demonstrated that RFAMCC preserved semantic consistency under original, compressed, and cropped conditions. It achieved correlation scores of 0.312, 0.308, and 0.301, as well as face retrieval accuracies of 83.7%, 82.4%, and 80.9%, respectively. In privacy-sensitive evaluations, gender and age inference attacks attained low success rates of 21.4% and 23.1%, indicating strong resistance to attribute leakage. Overall, RFAMCC effectively balances anonymization strength and reversibility, providing a practical solution for privacy-preserving and legally compliant facial data sharing.