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Attentive filtering networks for audio replay attack detection

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Original languageEnglish
Title of host publication2019 IEEE International Conference on Acoustics, Speech and Signal Processing
Pages6316-6320
ISBN (Electronic)9781538646588
DOIs
Publication statusE-pub ahead of print - 17 Apr 2019
Event44th International Conference on Acoustics, Speech, and Signal Processing: Signal Processing: Empowering Science and Technology for Humankind - Brighton , United Kingdom
Duration: 12 May 201917 May 2019
Conference number: 44
https://2019.ieeeicassp.org/

Conference

Conference44th International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP 2019
CountryUnited Kingdom
CityBrighton
Period12/05/1917/05/19
Internet address

Abstract

An attacker may use a variety of techniques to fool an automatic speaker verification system into accepting them as a genuine user. Anti-spoofing methods meanwhile aim to make the system robust against such attacks. The ASVspoof 2017 Challenge focused specifically on replay attacks, with the intention of measuring the limits of replay attack detection as well as developing countermeasures against them. In this work, we propose our replay attacks detection system - Attentive Filtering Network, which is composed of an attention-based filtering mechanism that enhances feature representations in both the frequency and time domains, and a ResNet-based classifier. We show that the network enables us to visualize the automatically acquired feature representations that are helpful for spoofing detection. Attentive Filtering Network attains an evaluation EER of 8.99% on the ASVspoof 2017 Version 2.0 dataset. With system fusion, our best system further obtains a 30% relative improvement over the ASVspoof 2017 enhanced baseline system.

    Research areas

  • ASVspoof, anti-spoofing, spoofing attack, replay attacks, automatic speaker verification

ID: 81930634