Imagine if every country reported the weather differently.
One city says it’s “hot,” another calls it “20 degrees,” and a third uses a scale no one else understands.
Without a shared standard, it would be impossible to compare conditions, accumulate data, and make reliable forecasts.
That’s the state of audio deepfake source tracing today.
A fragmented landscape
Each dataset and research project uses its own way of labeling systems, vocoders, and acoustic models.
This leads to:
- Benchmarks that can’t be compared,
- Results that are hard to interpret,
- Collaboration slowed down.
And when it comes to attributing attacks, this fragmentation becomes a real obstacle: without a common language, it’s extremely difficult to share intelligence on who might be behind a fake.
The need for a common language
The field needs a standardized ontology — a taxonomy that:
- Groups similar vocoders into families,
- Organizes acoustic models into meaningful categories,
- Provides a hierarchy that works across datasets.
This would make results comparable, transparent, and actionable — especially when different organizations collaborate to attribute and respond to attacks.
Why this matters
- Collaboration – Shared standards make it easier to exchange intelligence on deepfake methods and actors.
- Transparency – Regulators and enterprises get clearer answers, even without being technical experts.
- Stronger collective defense – Attribution becomes more reliable, and global coordination more effective.
Conclusion
Deepfake source tracing isn’t just about technology.
It’s about alignment.
By creating a common language, the field can move from fragmented efforts to a unified defense — where detection, tracing, and attribution all work together.
Because without a shared scale, forecasts will remain fragmented and incomplete.
But with the right standards, the entire field can finally read the same weather map — and identify not only the storm, but also where it came from.
Source :
Audio Deepfake Source Tracing using Multi-Attribute Open-Set Identification and Verification
Pierre Falez¹ Tony Marteau¹, Damien Lolive², Arnaud Delhay³
¹ Whispeak, France
² Univ Bretagne Sud, CNRS, IRISA, France
³ Univ Rennes, CNRS, IRISA, France
