FairReL：基于公平感知表示学习的Deepfake检测
FairReL: Deepfake Detection using Fairness-Aware Representation Learning
- University of Warwick（华威大学）
- University of Birmingham（伯明翰大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
FairReL是一种公平感知表示学习框架，通过SVD分解骨干网络及GCWD、SLMA两种互补损失，提升Deepfake检测的公平性与未见过数据集的AUC，降低子组FPR差异。
AI中文摘要：
尽管近期的Deepfake检测器达到了较高的整体准确率，但其错误在不同人口统计子组间的分布并不均匀，来自某些群体的真实人脸更常被误判为伪造。现有的公平感知检测器通常对整个特征表示进行正则化，却未识别或控制导致不公平预测的特定组件，这种粗略干预可能过度抑制有用的伪造线索，同时在特定组件子空间中保留人口统计结构。为解决该问题，本文识别出两个子组敏感组件：编码局部人脸和伪造模式的多尺度空间特征，以及使骨干网络适配不公平训练分布的微调诱导残差特征。本文提出FairReL，一种公平感知表示学习框架，针对上述两个组件采用专门的人口统计监督。FairReL使用经SVD分解的基础模型骨干网络分离微调诱导的残差表示，并引入两种互补损失：组条件小波去相关（GCWD）抑制空间小波子带间的子组不平衡结构，子空间局部均值对齐（SLMA）在残差表示的每个真实/伪造类别内对齐子组均值。在FF++、Celeb-DF、DFD和DFDC数据集上的实验表明，与最先进的公平感知检测器相比，FairReL将未见过数据集的AUC提高了3.9%，同时将子组FPR差异降低了10.2%。代码可在指定URL获取。
英文摘要：
Although recent deepfake detectors achieve high overall accuracy, their errors remain unevenly distributed across demographic subgroups, with real faces from certain groups more often misclassified as fake. Existing fairness-aware detectors typically regularise the entire feature representation, without identifying or controlling the specific components that drive unfair predictions. Such coarse intervention can over-suppress useful forgery cues while leaving demographic structure in component-specific subspaces. To address this, we identify two subgroup-sensitive components: multi-scale spatial features, which encode local facial and forgery patterns, and fine-tuning-induced residual features, which adapt the backbone to the unfair training distribution. We propose FairReL, a fairness-aware representation-learning framework that targets both components with dedicated demographic supervision. FairReL uses an SVD-decomposed foundation-model backbone to isolate the fine-tuning-induced residual representation, and introduces two complementary losses. Group-Conditional Wavelet Decorrelation (GCWD) suppresses subgroup-imbalanced structure across spatial wavelet sub-bands, while Subspace-Localised Mean Alignment (SLMA) aligns subgroup means within each real/fake class in the residual representation. Experiments on FF++, Celeb-DF, DFD and DFDC show that, against the state-of-the-art fairness-aware detector, FairReL improves unseen-dataset AUC by 3.9% while reducing subgroup FPR disparity by 10.2%. Code is available at https://github.com/xiaoman89/FairReL .
