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FIRC, A.; MALINKA, K.; HANÁČEK, P.
Original Title
Diffuse or Confuse: A Diffusion Deepfake Speech Dataset
English Title
Type
Paper in proceedings (conference paper)
Original Abstract
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.
English abstract
Keywords
deepfakes, deepfake speech, dataset, diffusion, detection
Key words in English
Authors
RIV year
2025
Released
11.12.2024
Publisher
GI - Group for computer science
Location
Darmstadt
ISBN
978-3-88579-749-4
Book
2024 International Conference of the Biometrics Special Interest Group (BIOSIG)
Pages from
1
Pages to
7
Pages count
URL
https://ieeexplore.ieee.org/document/10786752
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" }