提出零样本语音伪造溯源新框架，区分分布内与分布外场景表现更优。
Advancing Zero-Shot Open-Set Speech Deepfake Source Tracing
- 基于说话人验证思路，结合AAM损失与RegMixup增强特征表达。
- 分布内场景下少样本模型误差率低至13.11%，分布外场景零样本更胜一筹。
- 适用于未知伪造源的跨域溯源，适合安全检测与反欺诈研究者。
我们提出一种受说话人验证启发的新型零样本源溯源框架。将SSL-AASIST用于攻击分类，通过AAM损失和RegMixup增强嵌入表示，并确保训练攻击与指纹-试验对中的攻击互不重叠。在后端评分阶段，探索了零样本（余弦相似度、孪生网络）和少样本（MLP、孪生网络）方法。在最新提出的STOPA数据集上，开放集设置下的实验表明：在分布内（ID）场景中，少样本学习更具优势；而在分布外（OOD）场景中，零样本方法表现更佳。具体而言，在分布内试验中，少样本孪生网络与MLP的等错误率（EER）分别为17.72%和13.11%，显著优于零样本余弦相似度的29.91%；而在分布外试验中，零样本余弦相似度达到16.43%，优于少样本孪生网络的23.47%和MLP的21.57%。
原文摘要 · Abstract (English)
We propose a novel zero-shot source tracing framework inspired by speaker verification. We adapt SSL-AASIST for attack classification, enhancing embeddings with AAM loss and RegMixup, and ensure that training attacks are disjoint from those forming fingerprint-trial pairs. For backend scoring in attack verification, we explore both zero-shot approaches (cosine similarity and Siamese) and few-shot approaches (MLP and Siamese). Experiments on our recently introduced STOPA dataset with an open set setting show that few-shot learning provides advantages in the in-distribution (ID) scenario, while zero-shot approaches perform better in the out-of-distribution (OOD) scenario. In attack source verification with ID trials, few-shot Siamese and MLP achieve equal error rates (EER) of 17.72% and 13.11%, compared to 29.91% for zero-shot cosine scoring. Conversely, in OOD trials, zero-shot cosine scoring reaches 16.43%, outperforming few-shot Siamese at 23.47% and MLP at 21.57%.
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