首次揭示深度伪造检测中个体公平性失效问题，提出可集成的改进框架。
Rethinking Individual Fairness in Deepfake Detection
- 提出首个可通用的个体公平性增强框架
- 在主流数据集上显著提升个体公平性且保持高检测性能
- 适合关注公平性与鲁棒性的安全检测研究者
生成式AI模型大幅提升了合成媒体的真实感，但其被用于制造复杂深度伪造内容，带来重大风险。尽管深度伪造检测技术不断进步，公平性仍未得到充分重视，导致伪造特征可能针对特定群体产生偏见。现有研究多聚焦群体公平性，而个体公平性（即对相似个体应有相似预测）尚未被深入探索。本文首次发现，个体公平性的原始原则在深度伪造检测中根本失效，揭示了此前文献未被关注的关键缺口。为此，我们提出了首个可通用的框架，可无缝集成至现有检测器中以增强个体公平性与泛化能力。在主流深度伪造数据集上的大量实验表明，该方法显著提升了个体公平性，同时维持了强大的检测性能，优于当前最优方法。代码已开源：https://github.com/Purdue-M2/Individual-Fairness-Deepfake-Detection。
原文摘要 · Abstract (English)
Generative AI models have substantially improved the realism of synthetic media, yet their misuse through sophisticated DeepFakes poses significant risks. Despite recent advances in deepfake detection, fairness remains inadequately addressed, enabling deepfake markers to exploit biases against specific populations. While previous studies have emphasized group-level fairness, individual fairness (i.e., ensuring similar predictions for similar individuals) remains largely unexplored. In this work, we identify for the first time that the original principle of individual fairness fundamentally fails in the context of deepfake detection, revealing a critical gap previously unexplored in the literature. To mitigate it, we propose the first generalizable framework that can be integrated into existing deepfake detectors to enhance individual fairness and generalization. Extensive experiments conducted on leading deepfake datasets demonstrate that our approach significantly improves individual fairness while maintaining robust detection performance, outperforming state-of-the-art methods. The code is available at https://github.com/Purdue-M2/Individual-Fairness-Deepfake-Detection.
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