冗余语义环境填充与误导学习用于公平深度伪造检测
Redundant Semantic Environment Filling via Misleading-Learning for Fair Deepfake Detection
AI总结：
针对深度伪造检测中的双重过拟合问题，提出误导学习策略，通过填充冗余语义环境提升人口统计公平性，并保持高检测性能与泛化能力。
摘要：
检测由深度伪造技术生成的伪造人脸对于维护数字通信中的信任和保护个人至关重要。然而，当前的检测器常常遭受双重过拟合：它们既过度专门化于特定的伪造指纹，又过度专门化于特定的人口统计属性。关键的是，大多数现有方法忽视了后者的问题，这导致公平性不佳：来自某些人口统计群体（如不同性别或族裔）的人脸因此更难被可靠地检测。为应对这一挑战，我们提出了一种名为误导学习的新策略，该策略用大量冗余环境填充潜在空间。通过使检测器接触足够丰富且均衡的高层信息以实现人口统计公平性，我们的方法在保持高检测性能水平的同时减轻了人口统计偏见。我们在公平性、域内检测、跨域泛化和鲁棒性方面进行了广泛评估。实验结果表明，与最先进的方法相比，我们的框架实现了更优的公平性和泛化能力。
英文摘要：
Detecting falsified faces generated by Deepfake technology is essential for safeguarding trust in digital communication and protecting individuals. However, current detectors often suffer from a dual-overfitting: they become overly specialized in both specific forgery fingerprints and particular demographic attributes. Critically, most existing methods overlook the latter issue, which results in poor fairness: faces from certain demographic groups, such as different genders or ethnicities, are consequently more difficult to reliably detect. To address this challenge, we propose a novel strategy called misleading-learning, which populates the latent space with a multitude of redundant environments. By exposing the detector to a sufficiently rich and balanced variety of high-level information for demographic fairness, our approach mitigates demographic bias while maintaining a high detection performance level. We conduct extensive evaluations on fairness, intra-domain detection, cross-domain generalization, and robustness. Experimental results demonstrate that our framework achieves superior fairness and generalization compared to state-of-the-art approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
