Overlapped Frequency-Distributed Network: Frequency-Aware Voice Spoofing Countermeasure

Sunmook Choi; Il Youp Kwak; Seungsang Oh

doi:10.21437/Interspeech.2022-657

Overlapped Frequency-Distributed Network: Frequency-Aware Voice Spoofing Countermeasure

Sunmook Choi, Il Youp Kwak, Seungsang Oh

Department of Mathematics

Research output: Contribution to journal › Conference article › peer-review

5 Citations (Scopus)

Abstract

Numerous IT companies around the world are developing and deploying artificial voice assistants via their products, but they are still vulnerable to spoofing attacks. Since 2015, the competition “Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof)” has been held every two years to encourage people to design systems that can detect spoofing attacks. In this paper, we focused on developing spoofing countermeasure systems mainly based on Convolutional Neural Networks (CNNs). However, CNNs have translation invariant property, which may cause loss of frequency information when a spectrogram is used as input. Hence, we propose models which split inputs along the frequency axis: 1) Overlapped Frequency-Distributed (OFD) model and 2) Non-overlapped Frequency-Distributed (Non-OFD) model. Using ASVspoof 2019 dataset, we measured their performances with two different activations; ReLU and Max feature map (MFM). The best performing model on LA dataset is the Non-OFD model with ReLU which achieved an equal error rate (EER) of 1.35%, and the best performing model on PA dataset is the OFD model with MFM which achieved an EER of 0.35%.

Original language	English
Pages (from-to)	3558-3562
Number of pages	5
Journal	Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Volume	2022-September
DOIs	https://doi.org/10.21437/Interspeech.2022-657
Publication status	Published - 2022
Event	23rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2022 - Incheon, Korea, Republic of Duration: 2022 Sept 18 → 2022 Sept 22

Bibliographical note

Funding Information:
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:
Copyright © 2022 ISCA.

Keywords

Deep learning
audio deep synthesis
countermeasure
fake audio detection
spoofing

ASJC Scopus subject areas

Language and Linguistics
Human-Computer Interaction
Signal Processing
Software
Modelling and Simulation

Access to Document

10.21437/Interspeech.2022-657

Cite this

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title = "Overlapped Frequency-Distributed Network: Frequency-Aware Voice Spoofing Countermeasure",

abstract = "Numerous IT companies around the world are developing and deploying artificial voice assistants via their products, but they are still vulnerable to spoofing attacks. Since 2015, the competition “Automatic Speaker Verification Spoofing and Countermeasures Challenge (ASVspoof)” has been held every two years to encourage people to design systems that can detect spoofing attacks. In this paper, we focused on developing spoofing countermeasure systems mainly based on Convolutional Neural Networks (CNNs). However, CNNs have translation invariant property, which may cause loss of frequency information when a spectrogram is used as input. Hence, we propose models which split inputs along the frequency axis: 1) Overlapped Frequency-Distributed (OFD) model and 2) Non-overlapped Frequency-Distributed (Non-OFD) model. Using ASVspoof 2019 dataset, we measured their performances with two different activations; ReLU and Max feature map (MFM). The best performing model on LA dataset is the Non-OFD model with ReLU which achieved an equal error rate (EER) of 1.35%, and the best performing model on PA dataset is the OFD model with MFM which achieved an EER of 0.35%.",

keywords = "Deep learning, audio deep synthesis, countermeasure, fake audio detection, spoofing",

author = "Sunmook Choi and Kwak, {Il Youp} and Seungsang Oh",

note = "Funding Information: 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: Copyright {\textcopyright} 2022 ISCA.; 23rd Annual Conference of the International Speech Communication Association, INTERSPEECH 2022 ; Conference date: 18-09-2022 Through 22-09-2022",

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AU - Choi, Sunmook

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N1 - Funding Information: 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: Copyright © 2022 ISCA.

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Overlapped Frequency-Distributed Network: Frequency-Aware Voice Spoofing Countermeasure

Abstract

Bibliographical note

Keywords

ASJC Scopus subject areas

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