Resmax: Detecting voice spoofing attacks with residual network and max feature map

Il Youp Kwak, Sungsu Kwag, Junhee Lee, Jun Ho Huh, Choong Hoon Lee, Youngbae Jeon, Jeonghwan Hwang, Ji Won Yoon

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The “2019 Automatic Speaker Verification Spoofing And Countermeasures Challenge” (ASVspoof) competition aimed to facilitate the design of highly accurate voice spoofing attack detection systems. the competition did not emphasize model complexity and latency requirements; such constraints are strict and integral in real-world deployment. Hence, most of the top performing solutions from the competition all used an ensemble approach, and combined multiple complex deep learning models to maximize detection accuracy - this kind of approach would sit uneasily with real-world deployment constraints. To design a lightweight system, we combined the notions of skip connection (from ResNet) and max feature map (from Light CNN), and evaluated the accuracy of the system using the ASVspoof 2019 dataset. With an optimized constant Q transform (CQT) feature, our single model achieved a replay attack detection equal error rate (EER) of 0.37% on the evaluation set, surpassing the top ensemble system from the competition that achieved an EER of 0.39%.

Original languageEnglish
Title of host publicationProceedings of ICPR 2020 - 25th International Conference on Pattern Recognition
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4837-4844
Number of pages8
ISBN (Electronic)9781728188089
DOIs
Publication statusPublished - 2020
Event25th International Conference on Pattern Recognition, ICPR 2020 - Virtual, Milan, Italy
Duration: 2021 Jan 102021 Jan 15

Publication series

NameProceedings - International Conference on Pattern Recognition
ISSN (Print)1051-4651

Conference

Conference25th International Conference on Pattern Recognition, ICPR 2020
Country/TerritoryItaly
CityVirtual, Milan
Period21/1/1021/1/15

Keywords

  • Voice assistant security
  • Voice presentation attack detection
  • Voice spoofing attack
  • Voice synthesis attack

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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