Replay Attack Detection Using Magnitude and Phase Information with Attention-based Adaptive Filters

Meng Liu; Longbiao Wang; Jianwu Dang; Seiichi Nakagawa; Haotian Guan; Xiangang Li

ICASSP 2019accepted0 citations

Replay Attack Detection Using Magnitude and Phase Information with Attention-based Adaptive Filters

Meng Liu, Longbiao Wang, Jianwu Dang, Seiichi Nakagawa, Haotian Guan, Xiangang Li

Abstract

Automatic Speech Verification (ASV) systems are highly vulnerable to spoofing attacks, and replay attack poses the greatest threat among various spoofing attacks. In this paper, we propose a novel multi-channel feature extraction method with attention-based adaptive filters (AAF). Original phase information, discarded by conventional feature extraction techniques after Fast Fourier Transform (FFT), is promising in distinguishing genuine from replay spoofed speech. Accordingly, phase and magnitude information are respectively extracted as phase channel and magnitude channel complementary features in our system. First, we make discriminative ability analysis on full frequency bands with F-ratio methods. Then attention-based adaptive filters are implemented to maximize capturing of high discriminative information on frequency bands, and the results on ASVspoof 2017 challenge indicate that our proposed approach achieved relative error reduction rates of 78.7% and 59.8% on development and evaluation dataset than the baseline method.

BibTeX

@inproceedings{icassp2019_replayattackdete,
  title = {Replay Attack Detection Using Magnitude and Phase Information with Attention-based Adaptive Filters},
  author = {Meng Liu and Longbiao Wang and Jianwu Dang and Seiichi Nakagawa and Haotian Guan and Xiangang Li},
  booktitle = {ICASSP 2019},
  year = {2019}
}