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

• URL: http://arxiv.org/abs/2505.19644v2
• Date: Thu, 05 Jun 2025 12:43:39 GMT
• Title: STOPA: A Database of Systematic VariaTion Of DeePfake Audio for Open-Set Source Tracing and Attribution
• Authors: Anton Firc, Manasi Chibber, Jagabandhu Mishra, Vishwanath Pratap Singh, Tomi Kinnunen, Kamil Malinka,
• Abstract summary: STOPA is a dataset for deepfake speech source tracing covering 8 AMs, 6 settings, and 700k samples from 13 synthesisers.<n> 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.<n>This control improves attribution accuracy, aiding forensic analysis, deepfake detection, and generative model transparency.
• Score: 6.860131654491485
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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