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Cited by 20Speech Recognition and SynthesisBiometric Identification and SecuritySpeech and Audio Processing

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One-class learning with adaptive centroid shift for audio deepfake detection

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Kim, Hyun Myung, Jang, Kangwook, Kim, Hoirin·2024·Interspeech 2024
DOI 10.21437/interspeech.2024-177
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Abstract

As speech synthesis systems continue to make remarkable advances in recent years, the importance of robust deepfake detection systems that perform well in unseen systems has grown.In this paper, we propose a novel adaptive centroid shift (ACS) method that updates the centroid representation by continually shifting as the weighted average of bonafide representations.Our approach uses only bonafide samples to define their centroid, which can yield a specialized centroid for one-class learning.Integrating our ACS with one-class learning gathers bonafide representations into a single cluster, forming wellseparated embeddings robust to unseen spoofing attacks.Our proposed method achieves an equal error rate (EER) of 2.19% on the ASVspoof 2021 deepfake dataset, outperforming all existing systems.Furthermore, the t-SNE visualization illustrates that our method effectively maps the bonafide embeddings into a single cluster and successfully disentangles the bonafide and spoof classes.

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Metadata

Authors
Kim, H. M., Jang, K., & Kim, H.
Affiliations
Korea Advanced Institute of Science and Technology (KR)
ORCID
Kim, Hyun Myung
Venue
Interspeech 2024
Publisher
ISCA
Published
2024-09-01
Pages
4853-4857
ISSN
2308-457X (ISSN-L)
DOI
10.21437/interspeech.2024-177
OpenAlex
W4402112426
Cited by
17
References
0
Citations by year
2026: 9 · 2025: 7 · 2024: 1
FWCI
5.02
Type
conference paper
Language
en
Open access
Licence
public-domain
Locations
  • Interspeech (USB) (publishedVersion, public-domain)
  • arXiv (Cornell University) (submittedVersion, public-domain)
  • arXiv (Cornell University)
Funders
  • Ministry of Science and ICT, South Korea (2022-0-00653)
  • Institute for Information and Communications Technology Promotion (2022-)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Speech Recognition and Synthesis
  • Physical Sciences › Computer Science › Signal Processing › Biometric Identification and Security
  • Physical Sciences › Computer Science › Signal Processing › Speech and Audio Processing
Keywords
audio deepfake detection, one-class learning, anti-spoofing, speech synthesis, ASVspoof 2021

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CoChat. (2026). Citation check: One-class learning with adaptive centroid shift for audio deepfake detection. https://app.cochat.ai/papers/10.21437/interspeech.2024-177
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Kim, H. M., Jang, K., & Kim, H. (2024). One-class learning with adaptive centroid shift for audio deepfake detection. Interspeech 2024. https://doi.org/10.21437/interspeech.2024-177