用真实世界音频伪造数据提升检测模型性能
Unmasking real-world audio deepfakes: A data-centric approach
- 聚焦数据质量，通过清洗、裁剪和增强优化数据集
- 在真实场景数据上将误报率降低63%
- 适合关注实际应用的AI安全研究者
真实世界中的音频深度伪造日益增多，但现有检测系统多基于科研用途数据集评估，难以应对现实挑战。为此，本文构建了一个新型真实世界音频深度伪造数据集。分析表明，这些真实样本对最先进检测模型也构成严峻考验。不同于增加模型复杂度，本文采用数据中心范式，通过数据集清洗、修剪和增强等策略提升模型鲁棒性与泛化能力。实验结果显示，在In-the-Wild数据集上，错误等价率（EER）相对降低55%，达到1.7%；在新提出的AI4T数据集上，EER降低63%。结果凸显数据驱动方法在真实场景深度伪造检测中的巨大潜力。代码与数据见：https://github.com/davidcombei/AI4T。
原文摘要 · Abstract (English)
The growing prevalence of real-world deepfakes presents a critical challenge for existing detection systems, which are often evaluated on datasets collected just for scientific purposes. To address this gap, we introduce a novel dataset of real-world audio deepfakes. Our analysis reveals that these real-world examples pose significant challenges, even for the most performant detection models. Rather than increasing model complexity or exhaustively search for a better alternative, in this work we focus on a data-centric paradigm, employing strategies like dataset curation, pruning, and augmentation to improve model robustness and generalization. Through these methods, we achieve a 55% relative reduction in EER on the In-the-Wild dataset, reaching an absolute EER of 1.7%, and a 63% reduction on our newly proposed real-world deepfakes dataset, AI4T. These results highlight the transformative potential of data-centric approaches in enhancing deepfake detection for real-world applications. Code and data available at: https://github.com/davidcombei/AI4T.
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