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Paper Abstract and Keywords
Presentation 2020-03-02 13:00
Vulnerability investigation of speaker verification against black-box adversarial attacks
Hiroto Kai, Sayaka Shiota, Hitoshi Kiya (TMU) EA2019-106 SIP2019-108 SP2019-55
Abstract (in Japanese) (See Japanese page) 
(in English) Recently,vulnerability against adversarial attacks is being feared for machine learning-based systems.Adversarial attacks are aimed to intentionally change results of systems based on machine learning algorithms. Data which a small noise is added to an original speech are called perturbation. The perturbation is estimated by using adversarial structured neural networks, and the generated data are almost indistinguishable by a human when compared to the original data. Biometric authentication systems which are used as a primarily means for user authentication in applications like internet banking and online shopping are also pointed out in terms of vulnerability against such adversarial attacks. The same can be said for speaker verification (SV), one of the biometric authentication,however its robustness has not been indicated experimentally. Therefore,we investigated the change in accuracy when attacking a system where the internal design is unexpected using adversarial attacks.In our experiments,we have evaluated the results when inputting generated adversarial examples and audios mixed with white noise to SV systems,and analyzed the vulnerability of speaker verification against adversarial attacks.From the results, the performances of SV systems became worse when the generated adversarial examples were inputted, compared with audio with white noise were inputted.
Keyword (in Japanese) (See Japanese page) 
(in English) adversarial attack / speaker verification / black-box attack / / / / /  
Reference Info. IEICE Tech. Rep., vol. 119, no. 441, SP2019-55, pp. 29-33, March 2020.
Paper # SP2019-55 
Date of Issue 2020-02-24 (EA, SIP, SP) 
ISSN Print edition: ISSN 0913-5685    Online edition: ISSN 2432-6380
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
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Conference Information
Committee SP EA SIP  
Conference Date 2020-03-02 - 2020-03-03 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa Industry Support Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SP 
Conference Code 2020-03-SP-EA-SIP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Vulnerability investigation of speaker verification against black-box adversarial attacks 
Sub Title (in English)  
Keyword(1) adversarial attack  
Keyword(2) speaker verification  
Keyword(3) black-box attack  
1st Author's Name Hiroto Kai  
1st Author's Affiliation Tokyo Metropolitan University (TMU)
2nd Author's Name Sayaka Shiota  
2nd Author's Affiliation Tokyo Metropolitan University (TMU)
3rd Author's Name Hitoshi Kiya  
3rd Author's Affiliation Tokyo Metropolitan University (TMU)
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Speaker Author-1 
Date Time 2020-03-02 13:00:00 
Presentation Time 90 minutes 
Registration for SP 
Paper # EA2019-106, SIP2019-108, SP2019-55 
Volume (vol) vol.119 
Number (no) no.439(EA), no.440(SIP), no.441(SP) 
Page pp.29-33 
Date of Issue 2020-02-24 (EA, SIP, SP) 

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