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Paper Abstract and Keywords
Presentation 2022-05-17 15:30
A study of adversarial example detection using the correlation between adversarial noise and JPEG compression-derived distortion
Kenta Tsunomori, Yuma Yamasaki, Minoru Kuribayashi, Nobuo Funabiki (Okayama Univ.), Isao Echizen (NII) IT2022-6 EMM2022-6
Abstract (in Japanese) (See Japanese page) 
(in English) Adversarial examples cause misclassification of image classifiers. Higashi et al. proposed a method to detect adversarial examples by applying noise reduction filters of different strengths to input images and observing changes in the output of the image classifier. This method used 14 different filters, which was computationally expensive. In this paper, we propose a method that demonstrates high detection accuracy with a small number of filters. Based on Higashi et al.'s report, JPEG compression is considered to be a suitable filter for denoising adversarial noises. In the proposed method, a distortion signal created from the difference of images before and after JPEG compression is used as a denoising filter.
Keyword (in Japanese) (See Japanese page) 
(in English) Adversarial examples / Convolutional neural network / JPEG compression / Scaling / JPEG compression-derived distortion / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 26, EMM2022-6, pp. 29-34, May 2022.
Paper # EMM2022-6 
Date of Issue 2022-05-10 (IT, EMM) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
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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)
Download PDF IT2022-6 EMM2022-6

Conference Information
Committee IT EMM  
Conference Date 2022-05-17 - 2022-05-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Gifu University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Information Security, Information Theory, Information Hiding, etc. 
Paper Information
Registration To EMM 
Conference Code 2022-05-IT-EMM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A study of adversarial example detection using the correlation between adversarial noise and JPEG compression-derived distortion 
Sub Title (in English)  
Keyword(1) Adversarial examples  
Keyword(2) Convolutional neural network  
Keyword(3) JPEG compression  
Keyword(4) Scaling  
Keyword(5) JPEG compression-derived distortion  
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1st Author's Name Kenta Tsunomori  
1st Author's Affiliation Okayama University (Okayama Univ.)
2nd Author's Name Yuma Yamasaki  
2nd Author's Affiliation Okayama University (Okayama Univ.)
3rd Author's Name Minoru Kuribayashi  
3rd Author's Affiliation Okayama University (Okayama Univ.)
4th Author's Name Nobuo Funabiki  
4th Author's Affiliation Okayama University (Okayama Univ.)
5th Author's Name Isao Echizen  
5th Author's Affiliation National Institute of Informatics (NII)
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Speaker Author-1 
Date Time 2022-05-17 15:30:00 
Presentation Time 25 minutes 
Registration for EMM 
Paper # IT2022-6, EMM2022-6 
Volume (vol) vol.122 
Number (no) no.25(IT), no.26(EMM) 
Page pp.29-34 
#Pages
Date of Issue 2022-05-10 (IT, EMM) 


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