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，加剧数字鸿沟与社会不公。然而，当前基于公平性增强的检测器常以牺牲检测精度为代价提升公平性。为应对这一挑战，我们提出一种双机制协同优化框架。该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，继而在特征层面缩小整体样本分布与各群体对应分布间的距离。实验结果表明，相较于其他方法，本框架在保持跨领域整体检测精度的同时，显著提升了组间与组内公平性。