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Paper Abstract and Keywords
Presentation 2020-11-25 11:10
GAN based feature-level supportive method for improved adversarial attacks on face recognition
Zhengwei Yin (USTC/Hosei Univ.), Kaoru Uchida (Hosei Univ.) BioX2020-35
Abstract (in Japanese) (See Japanese page) 
(in English) With the rapid development of deep neural networks (DNN), DNN-based face recognition technologies are also achieving great success and have been widely used in various applications which require high-accuracy and robustness. However, deep neural networks are known to be vulnerable to adversarial attacks, performed using images added with well-designed perturbations. To enhance security of DNN-based face recognition, we need to explore deeper the mechanisms of related technologies. In this paper, we propose a feature-level supportive method, BiasGAN, to improve the performance of universal adversarial attack methods. We insert this image to image translation preprocessor before conducting adversarial example generation. BiasGAN will search in the potential face space and can generate images with biased face feature, causing generated face images to be easier to perturb efficiently. Experimental results show that this approach improves both fooling ratio and average perturbation size significantly at different perturbation levels.
Keyword (in Japanese) (See Japanese page) 
(in English) Deep neural network / enerative adversarial network / Face recognition / Adversarial attack / / / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 247, BioX2020-35, pp. 1-6, Nov. 2020.
Paper # BioX2020-35 
Date of Issue 2020-11-18 (BioX) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction
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 BioX  
Conference Date 2020-11-25 - 2020-11-25 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To BioX 
Conference Code 2020-11-BioX 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) GAN based feature-level supportive method for improved adversarial attacks on face recognition 
Sub Title (in English)  
Keyword(1) Deep neural network  
Keyword(2) enerative adversarial network  
Keyword(3) Face recognition  
Keyword(4) Adversarial attack  
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1st Author's Name Zhengwei Yin  
1st Author's Affiliation University of Science and Technology of China/Hosei University (USTC/Hosei Univ.)
2nd Author's Name Kaoru Uchida  
2nd Author's Affiliation Hosei University (Hosei Univ.)
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Speaker Author-1 
Date Time 2020-11-25 11:10:00 
Presentation Time 25 minutes 
Registration for BioX 
Paper # BioX2020-35 
Volume (vol) vol.120 
Number (no) no.247 
Page pp.1-6 
#Pages
Date of Issue 2020-11-18 (BioX) 


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