SOURCE-LINKED INTELLIGENCE
ToolDF: Tool-Integrated Reasoning for Mixed-Authenticity Audio Deepfake Detection
Audio deepfake detection is commonly formulated as clip-level binary classification of single-domain audio. However, real-world manipulated audio can exhibit mixed authenticity, where genuine and manipulated cues coexist across temporal transitions, overlapping sources, or both. This setting requires not only detecting manipulated audio but also localizing the components that provide evidence for the decision. We propose ToolDF, a tool-integrated reasoning framework for mixed-authenticity audio deepfake detection. ToolDF employs an audio large language model as an orchestrator trained with sup
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-09-03T10:05:45.000Z
First collected: 2026-09-21T04:51:57.792Z. This is not the publication date.