Fairness is a core element in the trustworthy deployment of deepfake detection models, especially in the field of digital identity security. Biases in detection models toward different demographic groups, such as gender and race, may lead to systemic misjudgments, exacerbating the digital divide and social inequities. However, current fairness-enhanced detectors often improve fairness at the cost of detection accuracy. To address this challenge, we propose a dual-mechanism collaborative optimization framework. Our proposed method innovatively integrates structural fairness decoupling and global distribution alignment: decoupling channels sensitive to demographic groups at the model architectural level, and subsequently reducing the distance between the overall sample distribution and the distributions corresponding to each demographic group at the feature level. Experimental results demonstrate that, compared with other methods, our framework improves both inter-group and intra-group fairness while maintaining overall detection accuracy across domains.

翻译：公平性是深度伪造检测模型可信部署的核心要素，尤其在数字身份安全领域。检测模型对不同人口统计群体（如性别、种族）的偏见可能导致系统性误判，加剧数字鸿沟和社会不公。然而，当前增强公平性的检测器往往以牺牲检测精度为代价来提升公平性。为应对这一挑战，我们提出了一种双机制协同优化框架。所提方法创新性地融合了结构公平性解耦与全局分布对齐：在模型架构层面解耦对人口统计群体敏感的通道，随后在特征层面缩小整体样本分布与各人口统计群体对应分布之间的距离。实验结果表明，与其他方法相比，我们的框架在保持跨领域整体检测精度的同时，提升了组间与组内公平性。