Partially manipulating a sentence can greatly change its meaning. Recent work
shows that countermeasures (CMs) trained on partially spoofed audio can
effectively detect such spoofing. However, the current understanding of the
decision-making process of CMs is limited. We utilize Grad-CAM and introduce a
quantitative analysis metric to interpret CMs' decisions. We find that CMs
prioritize the artifacts of transition regions created when concatenating bona
fide and spoofed audio. This focus differs from that of CMs trained on fully
spoofed audio, which concentrate on the pattern differences between bona fide
and spoofed parts. Our further investigation explains the varying nature of
CMs' focus while making correct or incorrect predictions. These insights
provide a basis for the design of CM models and the creation of datasets.
Moreover, this work lays a foundation of interpretability in the field of
partial spoofed audio detection that has not been well explored previously.

局部篡改句子可以大大改变其意义。最近的研究表明，训练在部分伪造音频上的对策可以有效地检测此类篡改。然而，目前对对策决策过程的理解有限。我们利用 Grad-CAM 并引入一种定量分析度量来解释对策的决策。我们发现对策优先考虑当连接真实和伪造音频时产生的过渡区域的伪迹。这种聚焦与仅在完全伪造音频上训练的对策不同，后者侧重于真实和伪造部分之间的模式差异。我们的进一步研究解释了在正确或错误预测时对策聚焦的差异性质。这些观察结果为对策模型的设计和数据集的创建提供了一个基础。此外，这项工作为之前未曾深入探索的局部篡改音频检测领域的解释性奠定了基础。