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AI Research
arXiv CS.AI·Jun 10·5 min read
What Do Deepfake Speech Detectors Actually Hear?
TL;DR
Deepfake speech detectors often lack transparency, making it unclear why certain audio samples are flagged. An audio-native explainability pipeline using Integrated Gradients was developed to identify decision evidence over time in three WavLM-based detectors.
✦ Why It Matters
Engineers can leverage these insights to enhance the transparency and effectiveness of deepfake detection systems.
Key Takeaways
- Adopt Integrated Gradients for model explainability in audio processing.
- Differentiate between cues used by various deepfake detectors.
- Validate model decisions by masking critical audio features.
- Enhance transparency in AI systems to build user trust.
How It Works
The proposed explainability pipeline employs Integrated Gradients, a technique that attributes the output of a model to its input features. By analyzing time-aligned self-supervised audio representations, the pipeline identifies which segments of the audio signal contribute most to the detection decision.
This allows researchers to visualize and understand the specific audio cues that each detector relies on, providing insights into their operational mechanisms.