Auditory Inspired Spatial Differentiation for Replay Spoofing Attack Detection
Buddhi Wickramasinghe, Eliathamby Ambikairajah, Julien Epps, Vidhyasaharan Sethu, Haizhou Li
Abstract
The security of Automatic Speaker Verification systems is greatly threatened by spoofing attacks of various kinds. Among them, replay attacks are noteworthy due to the ease with which they can be employed. Most countermeasures for replay attacks use subband features based on parallel filter banks. This paper explores the effect of `spatial differentiation' used in auditory system modelling to improve frequency selectivity and hence provide a more selective front-end for replay attack detection. Experiments were done using a parallel filter bank consisting of simple 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> order IIR bandpass filters following which, processing analogous to spatial differentiation was employed to obtain higher order stable IIR filters, in turn leading to highly selective filter banks. Two novel features based on spatially differentiated higher order filter bank have been proposed. Together they yield a relative improvement of 29.9% in replay speech detection over a constant Q transform based baseline system, when evaluated on the ASVspoof 2017 Version 2.0 database.
BibTeX
@inproceedings{icassp2019_auditoryinspired,
title = {Auditory Inspired Spatial Differentiation for Replay Spoofing Attack Detection},
author = {Buddhi Wickramasinghe and Eliathamby Ambikairajah and Julien Epps and Vidhyasaharan Sethu and Haizhou Li},
booktitle = {ICASSP 2019},
year = {2019}
}