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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26230%2F26%3A0196844" target="_blank" >RIV/00216305:26230/26:0196844 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.isca-archive.org/interspeech_2025/firc25_interspeech.html" target="_blank" >https://www.isca-archive.org/interspeech_2025/firc25_interspeech.html</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.21437/Interspeech.2025-2065" target="_blank" >10.21437/Interspeech.2025-2065</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

ISBN

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ISSN

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e-ISSN

2958-1796

Počet stran výsledku

5

Strana od-do

1553-1557

Název nakladatele

International Speech Communication Association

Místo vydání

Rotterdam, The Netherlands

Místo konání akce

Rotterdam

Datum konání akce

17. 8. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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