Abstract
Audio deepfake detection systems trained on one dataset often fail when deployed on data from different sources due to distributional shifts in recording conditions, synthesis methods, and acoustic environments. We present a modular pipeline for unsupervised domain adaptation that combines pre-trained Wav2Vec 2.0 embeddings with statistical transformations to improve cross-domain generalization without requiring labeled target data. Our approach applies power transformation for feature normalization, ANOVA-based feature selection, joint PCA for domain-agnostic dimensionality reduction, and CORAL alignment to match source and target covariance structures before classification via logistic regression. We evaluate on two cross-domain transfer scenarios: ASVspoof 2019 LA to Fake-or-Real (FoR) and FoR to ASVspoof, achieving 62.7--63.6\% accuracy with balanced performance across real and fake classes. Systematic ablation experiments reveal that feature selection (+3.5%) and CORAL alignment (+3.2%) provide the largest individual contributions, with the complete pipeline improving accuracy by 10.7% over baseline. While performance is modest compared to within-domain detection (94-96%), our pipeline offers transparency and modularity, making it suitable for deployment scenarios requiring interpretable decisions.
Abstract (translated)
音频深度伪造检测系统在不同数据集上的表现通常会下降，这是由于录音条件、合成方法和声学环境的分布变化造成的。我们提出了一种模块化的无监督领域适应管道，该管道结合了预训练的Wav2Vec 2.0嵌入与统计变换，以改善跨域泛化能力，并且不需要标记的目标数据。我们的方法包括用于特征归一化的幂转换、基于ANOVA的特征选择、用于领域无关维度减少的联合主成分分析（PCA）以及CORAL对齐来匹配源和目标协方差结构，在这些步骤之后通过逻辑回归进行分类。 我们在两个跨域传输场景中进行了评估：ASVspoof 2019 LA到Fake-or-Real (FoR) 和 FoR 到 ASVspoof，实现了62.7%至63.6%的准确率，并且真实和伪造类别的性能平衡。系统消融实验显示，特征选择（+3.5%）和CORAL对齐（+3.2%）提供了最大的单个贡献，而完整的管道相比于基准模型提升了10.7%的准确性。 尽管与领域内检测相比表现较弱（94-96%），我们的方法在可解释性和模块化方面具有优势，因此非常适合需要可解释决策的应用场景。
URL
https://arxiv.org/abs/2603.07935
