Generative Adversarial Network Facial Privacy Protection Model Combined with Facial Structure Modeling

Abstract

With the continuous development of intelligent vision technology, facial image anonymization plays an increasingly important role in public privacy protection and data compliance sharing. However, traditional methods have a contradiction between identity feature removal and image naturalness preservation, and are prone to distortion when dealing with semantic consistency issues such as posture. To this end, a structure identity separation guided facial anonymous generation method is constructed on the framework of generative adversarial networks. A structural perturbation module guided by differential privacy mechanisms is designed, and an attention guided feature weakening mechanism is introduced. Additionally, a style guided and structure preserving conditional generation network is integrated. In performance testing, the proposed method achieves identity consistency scores of 0.814 and 0.759 for frontal and lateral postures, respectively, with corresponding natural scores of 4.08 and 3.95. The inference delay, number of generated graphs per unit energy consumption, and peak memory of this method are 114.8 ms, 19.2 IpJ, and 1286 MB, respectively. The experimental results show that this method balances visual consistency and structural rationality while enhancing identity concealment, providing a feasible path and technical support for the secure generation and compliant application of private images.