Detecting Replay Attacks Using Multi-Channel Audio: A Neural Network-Based Method

被引：16

作者：

Gong, Yuan ^{[1
]}

Yang, Jian ^{[1
]}

Poellabauer, Christian ^{[1
]}

机构：

[1] Univ Notre Dame, Dept Comp Sci & Engn, Notre Dame, IN 46637 USA

来源：

IEEE SIGNAL PROCESSING LETTERS | 2020年 / 27卷

基金：

美国国家科学基金会;

关键词：

Microphone arrays; Task analysis; Feature extraction; Speech recognition; Array signal processing; Convolution; Microphone array signal processing; voice anti-spoofing; replay attack; beamforming; INSTANTANEOUS FREQUENCY; FEATURES;

D O I：

10.1109/LSP.2020.2996908

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

With the rapidly growing number of security-sensitive systems that use voice as the primary input, it becomes increasingly important to address these systems' potential vulnerability to replay attacks. Previous efforts to address this concern have focused primarily on single-channel audio. In this paper, we introduce a novel neural network-based replay attack detection model that further leverages spatial information of multi-channel audio and is able to significantly improve the replay attack detection performance.

引用

页码：920 / 924

页数：5

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