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    A comparative analysis of magnitude and phase spectrum-based features for replay attack detection by using deep learning
    (Gumushane University, 2025) Bekiryazıcı, Sule; Hanilçi, Cemal; Ozcan, Neyir
    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.
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    Enhancing Audio Replay Attack Detection with Silence-Based Blind Channel Impulse Response Estimation
    (Springer Science and Business Media Deutschland GmbH, 2026) Bekiryazıcı, Sule; Hanilçi, Cemal; Ozcan, Neyir
    Replay attacks pose a major threat to automatic speaker verification (ASV) systems, considerably degrading performance. Since replayed utterances are captured and reproduced using external microphones and speakers, they inherently reflect these acoustic influences. Such acoustic distortions serve as valuable cues for differentiating between genuine and spoofed speech, provided they can be effectively extracted and modeled. In this context, blind channel impulse response estimation has been shown to be an effective approach in replay attack detection, as it enables the characterization of the acoustic path through which the signal has propagated without requiring explicit knowledge of the original source or environment. Furthermore, prior studies have highlighted the importance of silence segments in this task, noting that these regions, being free of speech content, primarily capture the characteristics of the transmission channel. As such, silence segments offer a unique and robust opportunity for extracting channel-related features that are less influenced by speaker variability and phonetic content, thereby improving the discriminability between bonafide and replayed signals. In this paper, we argue that channel impulse response estimates derived from silence parts contain more discriminative information than those obtained from the entire signal or voiced parts. To exploit this insight, we propose to use log-magnitude channel frequency response estimated from the silence parts for replay attack detection. Experiments on ASVspoof 2019 and 2021 datasets show that utilizing silence-based channel response features reduces the EER from 4.21% to 3.17% and from 29.16% to 24.43%, respectively, compared to using the entire signal. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
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    Tekrar oynatma saldırılarının tespitinde genlik ve faz spektrumu tabanlı özniteliklerin derin öğrenme ile karşılaştırmalı analizi
    (2025) Bekiryazıcı, Şule; Hanilci, Cemal; Ozcan, Neyir
    Gelişen dijital teknolojiler ve artan güvenlik ihtiyaçları, biyometrik kimlik doğrulama sistemlerine olan ilgiyi ciddi biçimde artırmıştır. Bu sistemler, bireylerin fiziksel ya da davranışsal özelliklerini temel alarak yüksek güvenlik düzeyinde kimlik doğrulama sağlamaktadır. Otomatik konuşmacı doğrulama (Automatic Speaker Verification- ASV) sistemleri, kullanıcı dostu yapıları ve doğal etkileşim avantajlarıyla öne çıkmaktadır. Ancak bu sistemler, özellikle tekrar oynatma (replay) saldırıları gibi yapay giriş yöntemlerine karşı savunmasız kalabilmektedir. Bu tür saldırılar, önceden kaydedilen bir konuşma örneğinin yeniden çalınarak sistemin aldatılması temeline dayanmakta ve düşük maliyetli, uygulanabilir yöntemler olmaları nedeniyle önemli bir tehdit oluşturmaktadır. Bu çalışmada, konuşma sinyallerinden türetilen genlik ve faz spektrumu temelli özniteliklerin, tekrar oynatma saldırılarının tespiti üzerindeki etkileri sistematik olarak incelenmiştir. Çalışma kapsamında sekiz farklı genlik temelli ve üç farklı faz temelli öznitelik çıkarılmış, bu öznitelikler ASVspoof-2017, ASVspoof-2019 (Physical Access) ve ASVspoof-2021 (Physical Access) veri kümeleri üzerinde test edilmiştir. Her bir öznitelik kümesi, konuşma sinyalinin spektral veya fazsal özelliklerini farklı yönleriyle temsil etmekte; özellikle yankı, bozulma ve yeniden kaydetme gibi saldırı izlerini ortaya çıkarma potansiyeline sahiptir. Derin öğrenme temelli sınıflandırma için ResNet ve LCNN mimarileri kullanılmış, sistem başarımı EER (Equal Error Rate) ve t-DCF (tandem Detection Cost Function) metrikleri ile değerlendirilmiştir. Özellikle yüksek frekans bantlarında elde edilen ayırt edici spektral özelliklerin, sahte konuşma sinyallerinin tespitinde önemli bir katkı sunduğu gözlemlenmiştir. Elde edilen bulgular hem öznitelik çeşitliliği hem de mimari karşılaştırmaları açısından literatüre yeni ve bütüncül bir perspektif kazandırmaktadır.
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    Toward robust replay attack detection in Automatic Speaker Verification: A study of spectrum estimation and channel magnitude response modeling
    (Academic Press Ltd- Elsevier Science Ltd, 2026) Bekiryazici, Sule; Hanilci, Cemal; Ozcan, Neyir
    Automatic Speaker Verification (ASV) systems are increasingly adopted for biometric authentication but remain highly vulnerable to spoofing, particularly replay attacks. Existing countermeasures (CMs) for replay attack detection rely predominantly on discrete Fourier transform (DFT)-based spectral features, which are sensitive to noise and channel distortions common in physical access (PA) scenarios. This work presents the first comprehensive study of Channel Magnitude Response (CMR) representations for replay detection, explicitly analyzing the impact of spectrum estimation and feature design. The contribution of this work are fourfold: (i) CMR estimation is generalized beyond MFCCs to LFCC and CQCC features, with LFCC-based CMRs offering superior discrimination; (ii) alternative spectrum estimators - linear prediction (LP) and multitaper (MT) - are integrated into the CMR pipeline, yielding substantial gains over conventional DFT (iii) robustness is investigated under silence-free (voiced-only) conditions, mitigating known biases in ASVspoof datasets and (iv) a systematic evaluation of CMR is provided on the recently released ReplayDF corpus, a challenging benchmark combining replay and synthetic speech variability. Experiments on ASVspoof 2017, 2019, 2021, and ReplayDF using both baseline classifiers (ResNet18 and LCNN) and stronger models (Res2Net50 and SE-Res2Net50) show that the proposed approach consistently outperforms conventional features. Particularly, LFCC-CMR features with LP spectra achieve an Equal Error Rate (EER) as low as 1.34% on ASVspoof 2019 (PA), representing considerable relative improvements over traditional methods. Moreover, CMR-based systems retain high performance even when silent segments are removed, unlike conventional approaches. These results establish CMR with principled spectral modeling as a robust and generalizable framework for replay attack detection, opening new directions for resilient spoofing countermeasures.