AI-generated faces have enriched human life, such as entertainment, education, and art. However, they also pose misuse risks. Therefore, detecting AI-generated faces becomes crucial, yet current detectors show biased performance across different demographic groups. Mitigating biases can be done by designing algorithmic fairness methods, which usually require demographically annotated face datasets for model training. However, no existing dataset comprehensively encompasses both demographic attributes and diverse generative methods, which hinders the development of fair detectors for AI-generated faces. In this work, we introduce the AI-Face dataset, the first million-scale demographically annotated AI-generated face image dataset, including real faces, faces from deepfake videos, and faces generated by Generative Adversarial Networks and Diffusion Models. Based on this dataset, we conduct the first comprehensive fairness benchmark to assess various AI face detectors and provide valuable insights and findings to promote the future fair design of AI face detectors. Our AI-Face dataset and benchmark code are publicly available at https://github.com/Purdue-M2/AI-Face-FairnessBench.

翻译：AI生成人脸已在娱乐、教育、艺术等领域丰富了人类生活，但其滥用风险亦随之显现。因此，检测AI生成人脸变得至关重要，然而现有检测器在不同人口群体间表现出有偏的性能。通过设计算法公平性方法可缓解此类偏差，这类方法通常需要人口属性标注的人脸数据集进行模型训练。然而，现有数据集均未同时涵盖人口属性与多样化的生成方法，这阻碍了面向AI生成人脸的公平检测器的发展。本工作提出了AI-Face数据集——首个百万规模的人口属性标注AI生成人脸图像数据集，包含真实人脸、深度伪造视频人脸，以及由生成对抗网络和扩散模型生成的人脸。基于该数据集，我们构建了首个综合性公平基准以评估各类AI人脸检测器，并为推动未来AI人脸检测器的公平设计提供了有价值的见解与发现。我们的AI-Face数据集与基准代码已公开于https://github.com/Purdue-M2/AI-Face-FairnessBench。