Residual-based feature enhancement for forgery audio detection

Wei Zheng; Xia Ling; Han Hai

doi:10.1117/12.3005833

10 October 2023 Residual-based feature enhancement for forgery audio detection

Wei Zheng, Xia Ling, Han Hai

Proceedings Volume 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023); 127993B (2023) https://doi.org/10.1117/12.3005833
Event: 3rd International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 2023, Kuala Lumpur, Malaysia

Abstract

In recent years, speech synthesis technology has become increasingly advanced, leading to a proliferation of forged audio content on the internet, which poses significant threat to individuals and society. Many studies have utilized a range of deep learning-based techniques to differentiate fake audio content, but the features used in these studies are often limited in their rich and generalizable characteristics. In this paper, we propose a novel fake voice detection technology that utilizes the wav2vec2 model for feature extraction along with a custom-designed residual-based detection module to augment the detection of fake audio content with greater accuracy and precision. Additionally, we incorporate a data augmentation method to improve the performance of the model and enhance its ability to generalize. We trained our model on the ASVspoof2019 dataset and evaluated it on the LA and DF datasets of the ASVspoof2021 dataset. Supplementary experiments demonstrated that our approach achieved state-of-the-art detection performance and illustrated its effectiveness and applicability.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wei Zheng, Xia Ling, and Han Hai "Residual-based feature enhancement for forgery audio detection", Proc. SPIE 12799, Third International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2023), 127993B (10 October 2023); https://doi.org/10.1117/12.3005833

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available