An efficient identity-preserving and fast-converging hybrid generative adversarial network inversion framework

Abstract

In this paper, we present a novel Hybrid Generative Adversarial Network (HGAN) inversion framework that enables facial images to be rapidly inverted while preserving identity and personality characteristics. Accurate inversion of facial images requires high precision in computer vision and is critical to the success of future facial manipulations (age progression, regression, accessory, and hair stylization). However, existing methods often fail to preserve the personality characteristics of the real image, negatively affecting the accuracy of manipulations. In this context, our key contribution lies in using a transformer-based strategy to initiate the generator, which effectively models spatial relationships for detailed image processing. This approach is innovative because it leverages transformer structures to enhance image inversion tasks. Additionally, we introduce a novel loss function to enhance convergence speed and reliability, ensuring high accuracy in identity and personality trait preservation. Experimental results show that our method achieves a reconstruction accuracy of 93% and improves inversion time by 86%. This advancement could significantly impact facial manipulation technologies, laying the foundation for a technological breakthrough with potential applications in secure digital authentication systems and personal data protection. Our method may have a significant impact on privacy and security in future studies, contributing to the development of secure digital authentication systems and enhancing the protection of personal data. Therefore, our work is crucial for advancing the field of facial image manipulation and ensuring the privacy and security of personal data.