STOPA: A Database of Systematic VariaTion Of DeePfake Audio for Source Tracing and Attribution

STOPA: A Database of Systematic VariaTion Of DeePfake Audio for Source Tracing and Attribution

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A key research area in deepfake speech detection is source tracing - determining the origin of synthesised utterances. The approaches may involve identifying the acoustic model (AM), vocoder model (VM), or other generation-specific parameters. However, progress is limited by the lack of a dedicated, systematically curated dataset. To address this, we introduce STOPA, a systematically varied and metadata-rich dataset for deepfake speech source tracing, covering 8 AMs, 6 VMs, and diverse parameter settings across 700k samples from 13 distinct synthesisers. Unlike existing datasets, which often feature limited variation or sparse metadata, STOPA provides a systematically controlled framework covering a broader range of generative factors, such as the choice of the vocoder model, acoustic model, or pretrained weights, ensuring higher attribution reliability. This control improves attribution accuracy, aiding forensic analysis, deepfake detection, and generative model transparency.

A key research area in deepfake speech detection is source tracing - determining the origin of synthesised utterances. The approaches may involve identifying the acoustic model (AM), vocoder model (VM), or other generation-specific parameters. However, progress is limited by the lack of a dedicated, systematically curated dataset. To address this, we introduce STOPA, a systematically varied and metadata-rich dataset for deepfake speech source tracing, covering 8 AMs, 6 VMs, and diverse parameter settings across 700k samples from 13 distinct synthesisers. Unlike existing datasets, which often feature limited variation or sparse metadata, STOPA provides a systematically controlled framework covering a broader range of generative factors, such as the choice of the vocoder model, acoustic model, or pretrained weights, ensuring higher attribution reliability. This control improves attribution accuracy, aiding forensic analysis, deepfake detection, and generative model transparency.

source tracing, dataset, anti-spoofing, synthetic speech, deepfake

source tracing, dataset, anti-spoofing, synthetic speech, deepfake

FIRC, A.; CHHIBBER, M.; MISHRA, J.; SINGH, V.; KINNUNEN, T.; MALINKA, K.

17.08.2025

International Speech Communication Association

Rotterdam, The Netherlands

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH 2025

Interspeech

Nizozemsko

1553

1557

5

https://www.isca-archive.org/interspeech_2025/firc25_interspeech.html