FairAdapter: Detecting AI-generated Images with Improved Fairness
Feng Ding, Jun Zhang, Xinan He, Jianfeng Xu
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
BibTeX
@inproceedings{icassp2025_fairadapterdetec,
title = {FairAdapter: Detecting AI-generated Images with Improved Fairness},
author = {Feng Ding and Jun Zhang and Xinan He and Jianfeng Xu},
booktitle = {ICASSP 2025},
year = {2025}
}