用特定伪造痕迹专家模型实现可解释的语音深度伪造检测。
Toward Interpretable Speech Deepfake Detection using Artifact-Specific Experts and Calibrated Detection Scores
- 每个专家专精识别一种语音合成痕迹，输出可解释证据分数。
- 经校准后，专家能有效捕捉多种生成路径下的伪造信号。
- 适合需要透明决策的高风险场景，如司法或安全审查。
本文提出一种基于特定伪造痕迹专家模型的可解释语音深度伪造检测框架。不同于黑箱决策，该框架提供人类可理解的证据，对高风险场景至关重要。每个专家专门训练以检测一种语音合成痕迹，其输出被校准为对数似然比，作为可解释的证据分数。我们评估了五种特定痕迹专家，结果表明，经过适当校准，它们能有效捕捉目标伪造特征并生成有意义的证据。重要的是，每个专家仅判断其指定痕迹是否存在，而非直接做出真假分类。最终分类通过集成多个专家输出完成，同时保持可解释性，明确展示各专家对伪造判定的支持或反对程度。实验表明，这些专家能在多种生成路径中捕捉可解释的合成语音信号。
原文摘要 · Abstract (English)
In this work, we propose an interpretable framework for speech deepfake detection based on artifact-specific expert models. Rather than relying on black-box decisions, the framework provides human-understandable evidence, which is critical in high-stakes settings. Each expert is trained to detect a specific speech synthesis artifact, and its output is calibrated into a log-likelihood ratio that serves as an interpretable evidence score. We evaluate five artifact-specific experts and show that, with proper calibration, they can capture their target artifacts and produce meaningful evidence. Importantly, each expert estimates only the presence of its assigned artifact rather than directly performing the final decision. Their outputs are aggregated into an ensemble to produce the actual real-versus-fake classification, while maintaining interpretability by indicating how strongly each expert supports or contradicts a fake classification. Results show that artifact-specific experts capture interpretable signals of synthetic speech across multiple generation pipelines.
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