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cloud.google.com·7h ago
AI Research
arXiv CS.AI·Sep 4·5 min read
ToolDF: Tool-Integrated Reasoning for Mixed-Authenticity Audio Deepfake Detection
TL;DR
ToolDF is a novel framework for detecting mixed-authenticity audio deepfakes, achieving significant performance improvements in identifying and localizing manipulated audio components. It outperforms existing methods, providing interpretable evidence for its decisions.
✦ Why It Matters
Implement ToolDF to enhance your audio deepfake detection capabilities today.
Key Takeaways
- Explore ToolDF for advanced audio manipulation detection.
- Utilize the new mixed-authenticity benchmark for testing.
- Leverage localized evidence to improve decision-making.
- Access the public dataset to enhance your models.
How It Works
ToolDF employs an audio large language model as an orchestrator, trained on supervised tool-use trajectories. It analyzes audio scenes, performs source separation, and routes components to domain-specific experts.
The framework aggregates evidence from these experts to produce an interpretable verdict, allowing for both detection and localization of manipulated audio.