Year: 2025
Type: conference-paper
Abstract: The high-quality, realistic images generated by generative models pose significant challenges for exposing them. So far, data-driven deep neural networks have been justified as the most efficient forensics tools for the challenges. However, they may be over-fitted to certain semantics, resulting in considerable inconsistency in detection performance across different contents of generated samples. It could be regarded as an issue of detection fairness. In this paper, we propose a novel framework named Fairadapter to tackle the issue. In comparison with existing state-of-the-art methods, our model achieves improved fairness performance. Our project is vailable at https://github.com/AppleDogDog/FairnessDetection
Authors: Feng Ding, Jun Zhang, Xinan He, Jianfeng Xu
Institution: Nanchang University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G1918700671, G4421688228
OpenAlex ID: W4408352289