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Paper Abstract and Keywords
Presentation 2022-03-07 17:00
Extention of robust image classification system with Adversarial Example Detectors
Miki Tanaka, Takayuki Osakabe, Hitoshi Kiya (Tokyo Metro. Univ.) EMM2021-105
Abstract (in Japanese) (See Japanese page) 
(in English) In image classification with deep learning, there is a risk that an attacker can intentionally manipulate the prediction results of image classification by using images with a small designed noise, called adversarial examples. In this paper, we propose a robust image classification system against adversarial examples. In order to prevent the attack, there are two approaches: using a robust classifier, and using a detection method of adversarial examples. A robust image classification system with the combination of these two approaches was demonstrated to outperform conventional methods. In this paper, we extend the robust image classification system with the combination of the two approaches by using multiple features. The proposed method is more robust against various adversarial attacks and noise levels than the conventional one.
Keyword (in Japanese) (See Japanese page) 
(in English) Adversarial example / Machine learning / Deep learning / Adversarial detection / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 417, EMM2021-105, pp. 76-80, March 2022.
Paper # EMM2021-105 
Date of Issue 2022-02-28 (EMM) 
ISSN 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)
Download PDF EMM2021-105

Conference Information
Committee EMM  
Conference Date 2022-03-07 - 2022-03-08 
Place (in Japanese) (See Japanese page) 
Place (in English) (Primary: Online, Secondary: On-site) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image and Sound Quality, Metrics for Perception and Recognition, Human Auditory and Visual System, etc. 
Paper Information
Registration To EMM 
Conference Code 2022-03-EMM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Extention of robust image classification system with Adversarial Example Detectors 
Sub Title (in English)  
Keyword(1) Adversarial example  
Keyword(2) Machine learning  
Keyword(3) Deep learning  
Keyword(4) Adversarial detection  
1st Author's Name Miki Tanaka  
1st Author's Affiliation Tokyo Metropolitan University (Tokyo Metro. Univ.)
2nd Author's Name Takayuki Osakabe  
2nd Author's Affiliation Tokyo Metropolitan University (Tokyo Metro. Univ.)
3rd Author's Name Hitoshi Kiya  
3rd Author's Affiliation Tokyo Metropolitan University (Tokyo Metro. Univ.)
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Speaker Author-1 
Date Time 2022-03-07 17:00:00 
Presentation Time 25 minutes 
Registration for EMM 
Paper # EMM2021-105 
Volume (vol) vol.121 
Number (no) no.417 
Page pp.76-80 
Date of Issue 2022-02-28 (EMM) 

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