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FIRC, A.; MALINKA, K.; HANÁČEK, P.
Originální název
Diffuse or Confuse: A Diffusion Deepfake Speech Dataset
Anglický název
Druh
Stať ve sborníku v databázi WoS či Scopus
Originální abstrakt
Advancements in artificial intelligence and machine learning have significantly improved synthetic speech generation. This paper explores diffusion models, a novel method for creating realistic synthetic speech. We create a diffusion dataset using available tools and pretrained models. Additionally, this study assesses the quality of diffusion-generated deepfakes versus non-diffusion ones and their potential threat to current deepfake detection systems. Findings indicate that the detection of diffusion-based deepfakes is generally comparable to non-diffusion deepfakes, with some variability based on detector architecture. Re-vocoding with diffusion vocoders shows minimal impact, and the overall speech quality is comparable to non-diffusion methods.
Anglický abstrakt
Klíčová slova
deepfakes, deepfake speech, dataset, diffusion, detection
Klíčová slova v angličtině
Autoři
Rok RIV
2025
Vydáno
11.12.2024
Nakladatel
GI - Group for computer science
Místo
Darmstadt
ISBN
978-3-88579-749-4
Kniha
2024 International Conference of the Biometrics Special Interest Group (BIOSIG)
Strany od
1
Strany do
7
Strany počet
URL
https://ieeexplore.ieee.org/document/10786752
Plný text v Digitální knihovně
http://hdl.handle.net/
BibTex
@inproceedings{BUT189345, author="Anton {Firc} and Kamil {Malinka} and Petr {Hanáček}", title="Diffuse or Confuse: A Diffusion Deepfake Speech Dataset", booktitle="2024 International Conference of the Biometrics Special Interest Group (BIOSIG)", year="2024", pages="1--7", publisher="GI - Group for computer science", address="Darmstadt", doi="10.1109/BIOSIG61931.2024.10786752", isbn="978-3-88579-749-4", url="https://ieeexplore.ieee.org/document/10786752" }