A comparative analysis of magnitude and phase spectrum-based features for replay attack detection by using deep learning

Abstract

The rapid advancement of digital technologies and the growing demand for security have significantly increased interest in biometric authentication systems. These systems authenticate individuals based on their physical or behavioral traits, offering a high level of security. Among them, Automatic Speaker Verification (ASV) systems stand out due to their user-friendly design and natural interaction capabilities. However, these systems remain vulnerable to spoofing attacks, particularly replay attacks. Such attacks involve deceiving the system by playing back a previously recorded speech sample and are considered a major threat due to their low cost and practical feasibility. This study systematically investigates the effectiveness of amplitude and phase-based spectral features extracted from speech signals in detecting such replay attacks. A total of eight amplitude-based and three phase-based features were derived and evaluated on the ASVspoof-2017, ASVspoof-2019 (Physical Access), and ASVspoof-2021 (Physical Access) datasets. Each feature set represents the spectral or phase characteristics of speech from different perspectives, aiming to capture artifacts introduced by reverberation, distortion, and re-recording—common indicators of replay attacks. Two deep learning-based classifiers, ResNet and LCNN architectures, were employed for the classification task. System performance was assessed using Equal Error Rate (EER) and tandem Detection Cost Function (t-DCF) metrics. Experimental results demonstrate that spectral features, particularly those in higher frequency bands, provide strong discriminatory power in identifying spoofed speech signals. The findings contribute a comprehensive comparison of both feature diversity and model performance, offering a valuable perspective to the existing literature on robust countermeasures against replay attacks in Automatic Speaker Verification systems. © 2025, Gumushane University. All rights reserved.