Version 1
: Received: 9 September 2024 / Approved: 10 September 2024 / Online: 10 September 2024 (09:00:48 CEST)
How to cite:
Alshehri, A. S.; Almalki, D.; Albaradei, S. A. Audio Deep Fake Detection with Sonic Sleuth Model. Preprints2024, 2024090744. https://doi.org/10.20944/preprints202409.0744.v1
Alshehri, A. S.; Almalki, D.; Albaradei, S. A. Audio Deep Fake Detection with Sonic Sleuth Model. Preprints 2024, 2024090744. https://doi.org/10.20944/preprints202409.0744.v1
Alshehri, A. S.; Almalki, D.; Albaradei, S. A. Audio Deep Fake Detection with Sonic Sleuth Model. Preprints2024, 2024090744. https://doi.org/10.20944/preprints202409.0744.v1
APA Style
Alshehri, A. S., Almalki, D., & Albaradei, S. A. (2024). Audio Deep Fake Detection with Sonic Sleuth Model. Preprints. https://doi.org/10.20944/preprints202409.0744.v1
Chicago/Turabian Style
Alshehri, A. S., Danah Almalki and Somayah Abdullah Albaradei. 2024 "Audio Deep Fake Detection with Sonic Sleuth Model" Preprints. https://doi.org/10.20944/preprints202409.0744.v1
Abstract
Information dissemination and preservation are crucial for societal progress, especially in the technological age. While technology fosters knowledge sharing, it also risks spreading misinformation. Audio deep fakes, convincingly fabricated audio using Artificial intelligence (AI), exacerbate this issue. We present Sonic Sleuth, an AI model for detecting audio deepfakes. Our approach leverages Deep Learning (DL) for a robust detection model. Meticulous data preprocessing and rigorous experimentation with various models led to the implementation of the most effective solution with a custom CNN model. Our comprehensive testing resulted in a highly accurate model (98.27% accuracy, 0.016 EER) trained on a substantial dataset of real and synthetic audio. In addition to an 84.92% accuracy and 0.085 EER on an external dataset, these results demonstrate Sonic Sleuth’s potential as a powerful tool against audio misinformation.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
