Deepfake Audio Detection in Voice Authentication: A Spectral and CNN-Based Comprehensive Review

Ali Osman Mohammed Salih; Abdelmajid Hassan Mansour Emam; Alwalid Bashier Gism Elseed Ahmed; Mahmoud Khalifa; Abdelrazig Suliman; Nissrein Babiker Mohammed Babiker

doi:10.48084/etasr.13400

Authors

Ali Osman Mohammed Salih Department of Information Systems and Cyber Security, College of Computing and Information Technology, University of Bisha, Bisha 61922, P.O Box: 551, Saudi Arabia
Abdelmajid Hassan Mansour Emam Department of Information Technology, College of Computing and Information Technology - Khulais, University of Jeddah, 2841 - Ad Duf, Khulays 25535 - 7419, Saudi Arabia
Alwalid Bashier Gism Elseed Ahmed Department of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha 61922, P.O Box: 551, Saudi Arabia
Mahmoud Khalifa Department of Computer Science and Artificial Intelligence, College of Computing and Information Technology, University of Bisha, Bisha 61922, P.O Box: 551, Saudi Arabia
Abdelrazig Suliman Department of Information Systems and Cyber Security, College of Computing and Information Technology, University of Bisha, Bisha 61922, P.O Box: 551, Saudi Arabia
Nissrein Babiker Mohammed Babiker Department of Information Systems and Cyber Security, College of Computing and Information Technology, University of Bisha, Bisha 61922, P.O Box: 551, Saudi Arabia

Volume: 15 | Issue: 6 | Pages: 29824-29832 | December 2025 | https://doi.org/10.48084/etasr.13400

Received: 14 July 2025 | Revised: 29 August 2025 | Accepted: 9 September 2025 | Online: 30 October 2025

Corresponding author: Ali Osman Mohammed Salih

Abstract

As voice authentication systems become increasingly integral to critical domains such as banking, smart assistants, and remote identity verification, they face escalating threats from AI-generated audio, commonly referred to as deepfakes. These synthetic voices, produced through advanced text-to-speech and voice conversion technologies, can convincingly imitate human speech, thereby undermining the reliability and security of authentication frameworks. This study provides a comprehensive review of spectral-based techniques for deepfake audio detection, highlighting the roles of spectrograms, Mel-Frequency Cepstral Coefficients (MFCC), and Constant-Q Transform (CQT) in exposing time-frequency anomalies. The integration of Convolutional Neural Network (CNN)-based spoof detection modules before identity verification is identified as a critical architectural strategy to enhance system resilience. This review also outlines the prevailing challenges, including vulnerability due to emerging generative models, limited interpretability of deep learning classifiers, and decreased robustness under realistic or noisy conditions. To advance the field, this study emphasizes promising research directions such as hybrid modeling approaches, adversarial training techniques, and the development of multilingual open-access deepfake audio datasets. By critically synthesizing existing research, this review aims to inform the design of more robust, generalizable, and transparent voice authentication systems capable of surviving the evolving landscape of audio-based threats.

Keywords:

audio deepfakes, voice authentication, spoof detection, spectral features, CNN, ASV spoof

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