音频深度伪造检测中的性别公平性：性能与差异分析
Gender Fairness in Audio Deepfake Detection: Performance and Disparity Analysis
AI总结：
本文分析了音频深度伪造检测模型中性别依赖的性能和公平性，使用ASVspoof 5数据集训练ResNet-18分类器，并通过四种音频特征评估性能，结合公平性指标揭示性别差异。
摘要：
音频深度伪造检测旨在从人工智能生成的声音中检测真实人类声音，已成为语音生物识别系统中的重要问题。随着合成语音质量的提升，其被用于非法活动如身份伪造和冒充的可能性增加。尽管近年来在音频深度伪造检测领域取得了显著进展，但性别偏见问题仍处于初级阶段。本文对音频深度伪造检测模型中的性别依赖性能和公平性进行了全面分析。我们使用ASVspoof 5数据集训练了ResNet-18分类器，并在四种不同的音频特征上评估检测性能，并与基线AASIST模型进行比较。除了常规指标如等误差率（EER%）外，我们还纳入了五个已建立的公平性指标，以量化模型中的性别差异。我们的结果表明，即使性别间总体EER差异看起来较低，公平性意识评估揭示了被汇总性能指标掩盖的误差分布差异。这些发现表明，依赖标准指标是不可靠的，而公平性指标为特定人口群体的失败模式提供了关键见解。本文强调了公平性意识评估在开发更加公平、稳健和可信的音频深度伪造检测系统中的重要性。
英文摘要：
Audio deepfake detection aims to detect real human voices from those generated by Artificial Intelligence (AI) and has emerged as a significant problem in the field of voice biometrics systems. With the ever-improving quality of synthetic voice, the probability of such a voice being exploited for illicit practices like identity thest and impersonation increases. Although significant progress has been made in the field of Audio Deepfake Detection in recent times, the issue of gender bias remains underexplored and in its nascent stage In this paper, we have attempted a thorough analysis of gender dependent performance and fairness in audio deepfake detection models. We have used the ASVspoof 5 dataset and train a ResNet-18 classifier and evaluate detection performance across four different audio features, and compared the performance with baseline AASIST model. Beyond conventional metrics such as Equal Error Rate (EER %), we incorporated five established fairness metrics to quantify gender disparities in the model. Our results show that even when the overall EER difference between genders appears low, fairness-aware evaluation reveals disparities in error distribution that are obscured by aggregate performance measures. These findings demonstrate that reliance on standard metrics is unreliable, whereas fairness metrics provide critical insights into demographic-specific failure modes. This work highlights the importance of fairness-aware evaluation for developing a more equitable, robust, and trustworthy audio deepfake detection system.
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。
