Light-weight Frequency Information Aware Neural Network Architecture for Voice Spoofing Detection

Sunmook Choi, Seungsang Oh, Jonghoon Yang, Yerin Lee, Il Youp Kwak

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

3 Citations (Scopus)

Abstract

The voice assistant market is overgrowing, and mainstream services like Bixby (Samsung), Alexa (Amazon), and Siri (Apple) are quickly being upgraded to support advanced commands. Such capabilities make them lucrative targets for attackers to exploit. Voice spoofing attacks involve recording voice commands of a target victim and simply replaying them through a loudspeaker. The "2019 Automatic Speaker Verification Spoofing And Countermeasures Challenge"(ASVspoof) competition aims to facilitate the design of highly accurate voice spoofing attack detection systems. However, most of the presented models do not take frequency-level modeling into account in their modeling architecture and do not consider model complexity. To design a light-weight system with frequency-level modeling, we propose two systems: 1) Double Depthwise Separable (DDWS) convolution and 2) BC-ResNet with max feature map (MFM) activation (BC-ResMax). We evaluate the accuracy by equal error rate (EER) using the ASVspoof 2019 dataset. Our single models of parallel DDWS, sequential DDWS, and BC-ResMax model achieved spoofing attack detection EER of 2.63%, 2.08% and 2.59% in the LA dataset, and 0.47%, 0.63% and 0.49% in the PA dataset, achieving comparable performance with other top ensemble systems from the competition. Furthermore, parallel DDWS, sequential DDWS, and BC-ResMax used only 45K, 28K and 29K numbers of parameters which are far fewer than existing models.

Original languageEnglish
Title of host publication2022 26th International Conference on Pattern Recognition, ICPR 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages477-483
Number of pages7
ISBN (Electronic)9781665490627
DOIs
Publication statusPublished - 2022
Event26th International Conference on Pattern Recognition, ICPR 2022 - Montreal, Canada
Duration: 2022 Aug 212022 Aug 25

Publication series

NameProceedings - International Conference on Pattern Recognition
Volume2022-August
ISSN (Print)1051-4651

Conference

Conference26th International Conference on Pattern Recognition, ICPR 2022
Country/TerritoryCanada
CityMontreal
Period22/8/2122/8/25

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No. NRF-2020R1C1C1A01013020) and Institute for Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (No.2019-0-00033, 50%, Study on Quantum Security Evaluation of Cryptography based on Computational Quantum Complexity).

Publisher Copyright:
© 2022 IEEE.

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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