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

[1]

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

[2]

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

[3]

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

[4]

Ameer Hamza, Abdul Rehman Rehman Javed, Farkhund Iqbal, Natalia Kryvinska, Ahmad S. Almadhor, Zunera Jalil, and Rouba Borghol. 2022. Deepfake Audio Detection via MFCC Features Using Machine Learning. IEEE Access 10 (2022), 134018–134028. https://doi.org/10.1109/ACCESS.2022.3231480

[5]

Jungil Kong, Jaehyeon Kim, and Jaekyoung Bae. 2020. HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis. In Adv. Neural Inf. Process. Syst., Vol. 33. Curran Associates, Inc., 17022–17033.

[6]

Juan-Miguel López-Gil, Rosa Gil, and Roberto García. 2022. Do Deepfakes Adequately Display Emotions? A Study on Deepfake Facial Emotion Expression. Comput. Intell. Neurosci. 2022 (Oct. 2022), e1332122. https://doi.org/10.1155/2022/1332122

[7]

Daniele Mari, Federica Latora, and Simone Milani. 2022. The Sound of Silence: Efficiency of First Digit Features in Synthetic Audio Detection. In 2022 IEEE Int. Workshop Inf. Forensics Secur. WIFS. 1–6. https://doi.org/10.1109/WIFS55849.2022.9975404

[8]

Rahul T. P, P. R. Aravind, Ranjith C, Usamath Nechiyil, and Nandakumar Paramparambath. 2020. Audio Spoofing Verification Using Deep Convolutional Neural Networks by Transfer Learning. https://doi.org/10.48550/arXiv.2008.03464 arxiv:arXiv:2008.03464

[9]

Nishant Subramani and Delip Rao. 2020. Learning Efficient Representations for Fake Speech Detection. Proc. AAAI Conf. Artif. Intell. 34, 04 (April 2020), 5859–5866. https://doi.org/10.1609/aaai.v34i04.6044

[10]

Hemlata Tak, Jose Patino, Massimiliano Todisco, Andreas Nautsch, Nicholas Evans, and Anthony Larcher. 2021. End-to-End Anti-Spoofing with RawNet2. In ICASSP 2021 - 2021 IEEE Int. Conf. Acoust. Speech Signal Process. ICASSP. 6369–6373. https://doi.org/10.1109/ICASSP39728.2021.9414234

[11]

Hemlata Tak, Massimiliano Todisco, Xin Wang, Jee-weon Jung, Junichi Yamagishi, and Nicholas Evans. 2022. Automatic Speaker Verification Spoofing and Deepfake Detection Using Wav2vec 2.0 and Data Augmentation. In Speak. Lang. Recognit. Workshop Odyssey 2022. ISCA, 112–119. https://doi.org/10.21437/Odyssey.2022-16

[12]

Xu Tan, Tao Qin, Frank Soong, and Tie-Yan Liu. 2021. A Survey on Neural Speech Synthesis. https://doi.org/10.48550/arXiv.2106.15561 arxiv:arXiv:2106.15561

[13]

Ville Vestman, Tomi Kinnunen, Rosa González Hautamäki, and Md Sahidullah. 2020. Voice Mimicry Attacks Assisted by Automatic Speaker Verification. Computer Speech & Language 59 (Jan. 2020), 36–54. https://doi.org/10.1016/j.csl.2019.05.005

Digital Library

[14]

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

[15]

Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He. 2021. A Comprehensive Survey on Transfer Learning. Proc. IEEE 109, 1 (Jan. 2021), 43–76. https://doi.org/10.1109/JPROC.2020.3004555

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

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

Recommendations

Transferring Audio Deepfake Detection Capability across Languages
WWW '23: Proceedings of the ACM Web Conference 2023

The proliferation of deepfake content has motivated a surge of detection studies. However, existing detection methods in the audio area exclusively work in English, and there is a lack of data resources in other languages. Cross-lingual deepfake ...
GAN-based anomaly detection: A review
Graphical abstract

Display Omitted
Highlights
- This review reconsiders the anomaly and gives criteria and challenges for anomaly detection.
Abstract
Supervised learning algorithms have shown limited use in the field of anomaly detection due to the unpredictability and difficulty in acquiring abnormal samples. In recent years, unsupervised or semi-supervised anomaly-detection ...
Multiresolution Decomposition Analysis via Wavelet Transforms for Audio Deepfake Detection
Speech and Computer
Abstract
Voice and face recognition are becoming omnipresent, and the need for secure biometric technologies increases as technologies like deepfake are making it increasingly harder to spot fake generated content. To improve current audio spoofing ...

Comments

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

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 September 2023

Check for updates

Author Tags

Qualifiers

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

1
Total Citations
View Citations
241
Total Downloads

Downloads (Last 12 months)158
Downloads (Last 6 weeks)11

Reflects downloads up to 13 Jan 2025

Other Metrics

View Author Metrics

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

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents