量化语音伪造检测中各类证据对模型决策的贡献度
Evidence Subspace Projection: Measuring How Much Evidence Explains Deepfake Detection in Self-Supervised Speech Models
- 用神经元激活模式构建共享空间，表征证据与真伪标签
- 不同证据类型解释力差异显著，攻击类型影响最大
- 适用于分析自监督模型在伪造检测中的决策机制
自监督学习（SSL）模型广泛用于当前最先进的语音伪造检测任务，但其捕捉的特征与检测决策之间的直接定量关联仍不明确。为填补这一空白，我们提出证据子空间投影方法，将攻击类别、编码方式、性别、传输条件等证据因素以及真伪标签统一映射到由SSL模型神经元激活模式构建的共享空间中。通过将决策向量投影至各证据子空间，获得可量化每类证据解释力的标量比率。我们在多个数据集上评估了原始、微调和后训练状态下的SSL模型，结果验证了已有研究结论，并揭示了模型行为的新洞察。
原文摘要 · Abstract (English)
Self-supervised learning (SSL) models are widely used as feature extractors for state-of-the-art audio deepfake detection, but it remains unclear how to directly and quantitatively connect what SSL models capture to detection decisions. To address this gap, we propose Evidence Subspace Projection, a method that represents both evidence factors (e.g., attack category, codec, gender, transmission) and authenticity labels in a shared space constructed from SSL models' neuron activation patterns. By projecting the decision vector onto each evidence subspace, we obtain a scalar ratio that quantifies the explanatory power of each evidence type. We evaluate SSL models in raw, fine-tuned, and post-trained settings on multiple datasets. The results confirm findings from established studies, validating the proposed method, and reveal new insights into model behavior.
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