• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

A Robust Method for Speech Replay Attack Detection

Vol. 14, No. 1, January 31, 2020
10.3837/tiis.2020.01.010, Download Paper (Free):

Abstract

Spoofing attacks, especially replay attacks, pose great security challenges to automatic speaker verification (ASV) systems. Current works on replay attacks detection primarily focused on either developing new features or improving classifier performance, ignoring the effects of feature variability, e.g., the channel variability. In this paper, we first establish a mathematical model for replay speech and introduce a method for eliminating the negative interference of the channel. Then a novel feature is proposed to detect the replay attacks. To further boost the detection performance, four post-processing methods using normalization techniques are investigated. We evaluate our proposed method on the ASVspoof 2017 dataset. The experimental results show that our approach outperforms the competing methods in terms of detection accuracy. More interestingly, we find that the proposed normalization strategy could also improve the performance of the existing algorithms.


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Cite this article

[IEEE Style]
L. Lin, R. Wang, D. Yan and L. Dong, "A Robust Method for Speech Replay Attack Detection," KSII Transactions on Internet and Information Systems, vol. 14, no. 1, pp. 168-182, 2020. DOI: 10.3837/tiis.2020.01.010.

[ACM Style]
Lang Lin, Rangding Wang, Diqun Yan, and Li Dong. 2020. A Robust Method for Speech Replay Attack Detection. KSII Transactions on Internet and Information Systems, 14, 1, (2020), 168-182. DOI: 10.3837/tiis.2020.01.010.

[BibTeX Style]
@article{tiis:23227, title="A Robust Method for Speech Replay Attack Detection", author="Lang Lin and Rangding Wang and Diqun Yan and Li Dong and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2020.01.010}, volume={14}, number={1}, year="2020", month={January}, pages={168-182}}