训练数据性别比例决定检测模型偏见方向，后处理无法根本解决。
What You Train Is What You Get: Gender Bias, Training Composition, and Post-Hoc Mitigation in Audio Deepfake Detection
- 通过控制训练集性别组成，发现性别偏见由训练数据决定。
- 使用WavLM特征时，性别误差差距比对数谱图大3.0至4.3倍。
- 所有事后校准方法均无法消除1.317个百分点的性能差距。
音频深度伪造检测模型在整体准确率高时，仍可能在不同人口群体间存在显著性能差异。本文基于ASVspoof5数据集，在受控自定义划分下，训练了九组不同性别构成的攻击特异性模型（从纯女性到纯男性）。采用ResNet18分类器，结合对数谱图与WavLM-Base+特征，并评估六种事后阈值校准方法。结果表明，训练数据性别构成强烈预测偏见方向：少数性别在测试时表现更差。在相同训练条件下，WavLM-Base+特征导致的性别性能差距是日志谱图的3.0至4.3倍；平衡训练虽能降低日志谱图的偏见，却无法缓解WavLM的偏见。此外，所有六种校准策略（包括拥有完整测试标签的最优校准）均未能改变1.317个百分点的等错误率差距，证实阈值调整无法修复评分分布的根本差异。总体表明，音频深度伪造检测中的性别公平问题必须在训练阶段解决。
原文摘要 · Abstract (English)
Audio deepfake detection models determine whether speech is genuine or artificially generated, but high overall accuracy can mask substantial performance disparities across demographic groups. In this work, we investigate gender bias in audio deepfake detection using the ASVspoof5 dataset. We use ASVspoof5 under a controlled custom split designed to isolate gender-composition effects. We train attack-specific models on nine training sets with different gender compositions, ranging from female-only to male-only. We use a ResNet18 classifier with LogSpectrogram and WavLM-Base+ features, and we evaluated six post-hoc threshold calibration methods. Experimental results show that training data composition strongly predicts bias direction, with the underrepresented gender performing worse at test time. WavLM-Base+ features are shown to produce gender performance gaps 3.0 to 4.3 times larger than LogSpectrogram under identical training conditions, and balanced training is found to reduce LogSpectrogram bias but leave WavLM bias largely intact. Moreover, all six calibration strategies, including Oracle calibration with full test-set label access, leave the Equal Error Rate gap unchanged at 1.317 pp, confirming that threshold adjustment cannot correct underlying score distribution disparities. Overall, these findings suggest that gender fairness in audio deepfake detection must be addressed at training time, as post-hoc methods can only partially mitigate the resulting disparities
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