无需训练即可定位音频深度伪造来源并识别未知模型
TADA: Training-free Attribution and Out-of-Domain Detection of Audio Deepfakes
- 基于预训练自监督模型与kNN，实现无训练源追踪
- 跨5个数据集达0.93 F1得分，未知模型检测0.84 F1
- 绿色AI方法，适合安全审计与对抗深度伪造
深度伪造检测在音频、文本和图像领域已获广泛关注，现有方法能高精度区分真伪。但精准溯源——如识别伪造所用系统或模型——仍研究不足。本文提出一种无需训练的绿色AI方法TADA，完全基于k近邻（kNN）实现音频深度伪造模型归属与域外检测。利用预训练自监督学习（SSL）模型，我们发现同生成器样本可被自然分组，在五个深度伪造数据集上获得0.93 F1分数；同时对未见模型样本的域外检测也表现良好，F1得分为0.84。通过多维度分析进一步验证结果有效性。所有代码与数据协议已在开源仓库公开：https://github.com/adrianastan/tada/。
原文摘要 · Abstract (English)
Deepfake detection has gained significant attention across audio, text, and image modalities, with high accuracy in distinguishing real from fake. However, identifying the exact source--such as the system or model behind a deepfake--remains a less studied problem. In this paper, we take a significant step forward in audio deepfake model attribution or source tracing by proposing a training-free, green AI approach based entirely on k-Nearest Neighbors (kNN). Leveraging a pre-trained self-supervised learning (SSL) model, we show that grouping samples from the same generator is straightforward--we obtain an 0.93 F1-score across five deepfake datasets. The method also demonstrates strong out-of-domain (OOD) detection, effectively identifying samples from unseen models at an F1-score of 0.84. We further analyse these results in a multi-dimensional approach and provide additional insights. All code and data protocols used in this work are available in our open repository: https://github.com/adrianastan/tada/.
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