poster

GAN Discriminator based Audio Deepfake Detection

Authors:

Thien Phuc Doan,

Souhwan JungAuthors Info & Claims

WDC '23: Proceedings of the 2nd Workshop on Security Implications of Deepfakes and Cheapfakes

Pages 29 - 32

https://doi.org/10.1145/3595353.3595883

Published: 02 September 2023 Publication History

Abstract

Deepfake is a new technology that has emerged in recent times and is becoming one of the great challenges for society and individuals. In particular, scammers could use deepfake to start a phishing attack by cloning the victim’s voice and calling his related person with the fake audio. In this study, we propose a transfer learning model for detecting deepfake audio. This model introduces a method to leverage the discriminator of a GAN-based vocoder model to extract the front-end features of an unidentified audio sample, which helps the model to detect fake voices more easily. With a detection efficiency of up to 94%, we demonstrate that this transfer learning method is feasible.

References

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Su-Yu Chang, Kai-Cheng Wu, and Chia-Ping Chen. 2019. Transfer-Representation Learning for Detecting Spoofing Attacks with Converted and Synthesized Speech in Automatic Speaker Verification System. In Interspeech 2019. ISCA, 1063–1067. https://doi.org/10.21437/Interspeech.2019-2014

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Emanuele Conti, Davide Salvi, Clara Borrelli, Brian Hosler, Paolo Bestagini, Fabio Antonacci, Augusto Sarti, Matthew C. Stamm, and Stefano Tubaro. 2022. Deepfake Speech Detection Through Emotion Recognition: A Semantic Approach. In ICASSP 2022 - 2022 IEEE Int. Conf. Acoust. Speech Signal Process. ICASSP. 8962–8966. https://doi.org/10.1109/ICASSP43922.2022.9747186

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Youngsik Eom, Yeonghyeon Lee, Ji Sub Um, and Hoi Rin Kim. 2022. Anti-Spoofing Using Transfer Learning with Variational Information Bottleneck. In Interspeech 2022. ISCA, 3568–3572. https://doi.org/10.21437/Interspeech.2022-10200

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Digital Library

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Junichi Yamagishi, Xin Wang, Massimiliano Todisco, Md Sahidullah, Jose Patino, Andreas Nautsch, Xuechen Liu, Kong Aik Lee, Tomi Kinnunen, Nicholas Evans, and Héctor Delgado. 2021. ASVspoof 2021: Accelerating Progress in Spoofed and Deepfake Speech Detection. In 2021 Ed. Autom. Speak. Verification Spoofing Countermeas. Chall.ISCA, 47–54. https://doi.org/10.21437/ASVSPOOF.2021-8

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Cited By

Huang Q(2024)Who is Authentic Speaker?2024 29th International Conference on Automation and Computing (ICAC)10.1109/ICAC61394.2024.10718807(1-6)Online publication date: 28-Aug-2024
https://doi.org/10.1109/ICAC61394.2024.10718807
Purohit RShah APatil H(2024)GGMDDC: An Audio Deepfake Detection Multilingual Dataset2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)10.1109/APSIPAASC63619.2025.10848580(1-6)Online publication date: 3-Dec-2024
https://doi.org/10.1109/APSIPAASC63619.2025.10848580

Index Terms

GAN Discriminator based Audio Deepfake Detection
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction
  2. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Security and privacy
  1. Intrusion/anomaly detection and malware mitigation
    1. Social engineering attacks
      1. Spoofing attacks

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Information & Contributors

Information

Published In

cover image ACM Conferences

WDC '23: Proceedings of the 2nd Workshop on Security Implications of Deepfakes and Cheapfakes

July 2023

37 pages

ISBN:9798400702037

DOI:10.1145/3595353

Editors:
Simon S. Woo
Sungkyunkwan University, South Korea
,
Shahroz Tariq
CSIRO’s Data61, Australia
,
Sharif Abuadbba
CSIRO’s Data61, Australia
,
Kristen Moore
CSIRO’s Data61, Australia

Copyright © 2023 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Sponsors

SIGSAC: ACM Special Interest Group on Security, Audit, and Control

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 September 2023

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Author Tags

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Poster
Research
Refereed limited

Funding Sources

No.2020-0-00952, Development of 5G Edge Security Technology for Ensuring 5G+ Service Stability and Availability
No.2022-0-00495, On-Device Voice Phishing Call Detection

Conference

ASIA CCS '23

Sponsor:

SIGSAC

ASIA CCS '23: ACM Asia Conference on Computer and Communications Security

July 10 - 14, 2023

VIC, Melbourne, Australia

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Citations

Cited By

Huang Q(2024)Who is Authentic Speaker?2024 29th International Conference on Automation and Computing (ICAC)10.1109/ICAC61394.2024.10718807(1-6)Online publication date: 28-Aug-2024
https://doi.org/10.1109/ICAC61394.2024.10718807
Purohit RShah APatil H(2024)GGMDDC: An Audio Deepfake Detection Multilingual Dataset2024 Asia Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA ASC)10.1109/APSIPAASC63619.2025.10848580(1-6)Online publication date: 3-Dec-2024
https://doi.org/10.1109/APSIPAASC63619.2025.10848580

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