FairAdapter: Detecting AI-generated Images with Improved Fairness
This addresses fairness issues in AI-generated image detection, which is an incremental improvement over existing methods.
The paper tackles the problem of inconsistent detection performance across different content in AI-generated images, proposing FairAdapter to improve fairness in detection.
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: https://github.com/AppleDogDog/FairnessDetection