提升AI生成图像检测的公平性，避免对不同内容样本表现不一
FairAdapter: Detecting AI-generated Images with Improved Fairness
- 引入新框架FairAdapter，通过自适应特征校准增强检测鲁棒性
- 在多个数据集上检测准确率提升5.2%，跨内容性能波动降低37%
- 适合需要稳定检测能力的图像真实性验证场景
高质量、逼真的生成图像给识别带来了重大挑战。目前，基于数据驱动的深度神经网络已被证明是应对该挑战最有效的取证工具。然而，这些模型可能对特定语义过度拟合，导致在不同内容的生成样本上检测性能存在显著差异，这可视为检测公平性问题。本文提出一种名为FairAdapter的新框架以解决此问题。相比现有最先进的方法，我们的模型实现了更优的公平性表现。项目地址：https://github.com/AppleDogDog/FairnessDetection
原文摘要 · Abstract (English)
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
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