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Conference proceeding
Forensic Investigation of Synthetic Voice Spoofing Detection in Social App
ACMSE 2025: Proceedings of the 2025 ACM Southeast Conference, pp.263-268
ACM Other Conferences
ACMSE 2025: ACM Southeast Conference (Cape Girardeau, Missouri, USA, 04/24/2025–04/26/2025)
05/08/2025
Web of Science ID: WOS:001494357600032
Abstract
With the rapid growth of social applications, the misuse of synthetic voice generation technologies poses a significant security threat. Voice spoofing, where artificial voices are generated to impersonate real individuals, is a growing concern in various domains, including online communication, authentication, and social media interactions. This paper uses deep learning techniques to present a forensic investigation into the detection of synthetic voice spoofing within social apps. This study integrates a Convolutional Neural Network (CNN) with a Temporal Convolutional Network (TCN) in a hybrid architecture. A lightweight MobileNet CNN first extracts spatial features from Mel-Spectrograms, which are then analyzed by the TCN to capture sequential patterns. Using the fake-or-real (FoR) dataset, the for-norm dataset, this model achieved a training precision of 99.89% and validation accuracy of 99.79% and for-rerec dataset the model achieved a training precision of 99.79% and validation accuracy of 94.22%. Evaluation metrics, including the precision-recall curve with an average precision of 99% and the ROC curve with an AUC of 99%, underscore the model's robustness in distinguishing real from synthetic audio, offering a reliable solution for real-time deployment in resource-constrained environments.
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Details
- Title
- Forensic Investigation of Synthetic Voice Spoofing Detection in Social App
- Publication Details
- ACMSE 2025: Proceedings of the 2025 ACM Southeast Conference, pp.263-268
- Resource Type
- Conference proceeding
- Conference
- ACMSE 2025: ACM Southeast Conference (Cape Girardeau, Missouri, USA, 04/24/2025–04/26/2025)
- Publisher
- ACM
- Series
- ACM Other Conferences
- Number of pages
- 6
- Grant note
- National Centers of Academic Excellence in Cybersecurity Grant, National Security Agency: H98230-21-1-0326 National Science Foundation: 2333950
This research was funded by the National Centers of Academic Excellence in Cybersecurity Grant (H98230-21-1-0326), which is part of the National Security Agency. The National Science Foundation under NSF Grant 2333950 partially supports this work. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the NSF.
- Copyright
- © 2025 by the Association for Computing Machinery, Inc.
- Identifiers
- WOS:001494357600032; 99381356649206600
- Academic Unit
- Cybersecurity and Information Technology; Hal Marcus College of Science and Engineering
- Language
- English