ToolDF: Tool-Integrated Reasoning for Mixed-Authenticity Audio Deepfake Detection
Quick summary
arXiv:2609.03620v2 Announce Type: replace-cross Abstract: 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 audi
Key takeaways
- arXiv:2609.03620v2 Announce Type: replace-cross Abstract: 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.
Why it matters
“ToolDF: Tool-Integrated Reasoning for Mixed-Authenticity Audio Deepfake Detection” shows why AI risk cannot be reduced to answer accuracy. Access controls, logging, human approval and incident response need to be designed into the workflow from the start.
Member comments