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Paper Abstract and Keywords
Presentation 2024-01-19 13:30
[Invited Talk] Problem of Adversarial Attacks on CNN-based Image Classifiers and Countermeasures
Minoru Kuribayashi (Tohoku Univ.) IT2023-67 SIP2023-100 RCS2023-242
Abstract (in Japanese) (See Japanese page) 
(in English) It is well-known that discriminative models based on deep learning techniques may cause misclassification if adversarial perturbations which are difficult to perceive by humans are added to input signals. This can be understood as a potential vulnerability of machine learning technologies in general, due to the difference in cognitive mechanisms between humans and machines.
Since adversarial images are created by adding tiny noise, it has been studied to consider an approach to suppress the effect of the noise. When the adversarial image is filtered, the confidence vector output from the CNN image classifier changes according to the characteristics of the denoising filter. By focusing on these changes and extracting them as feature components for analysis, research has been conducted to detect adversarial images and to remove adversarial perturbations appropriately. In this talk, the threat of adversarial attacks on CNN-based image classifiers is explained, and discuss the countermeasures to protect the models against adversarial attacks.
Keyword (in Japanese) (See Japanese page) 
(in English) adversarial attack / CNN-based image classifier / denoising filter / detection of adversarial examples / adversarial purification / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 339, SIP2023-100, pp. 204-204, Jan. 2024.
Paper # SIP2023-100 
Date of Issue 2024-01-11 (IT, SIP, RCS) 
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)
Download PDF IT2023-67 SIP2023-100 RCS2023-242

Conference Information
Committee SIP IT RCS  
Conference Date 2024-01-18 - 2024-01-19 
Place (in Japanese) (See Japanese page) 
Place (in English)  
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SIP 
Conference Code 2024-01-SIP-IT-RCS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Problem of Adversarial Attacks on CNN-based Image Classifiers and Countermeasures 
Sub Title (in English)  
Keyword(1) adversarial attack  
Keyword(2) CNN-based image classifier  
Keyword(3) denoising filter  
Keyword(4) detection of adversarial examples  
Keyword(5) adversarial purification  
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Keyword(7)  
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1st Author's Name Minoru Kuribayashi  
1st Author's Affiliation Tohoku University (Tohoku Univ.)
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Speaker Author-1 
Date Time 2024-01-19 13:30:00 
Presentation Time 50 minutes 
Registration for SIP 
Paper # IT2023-67, SIP2023-100, RCS2023-242 
Volume (vol) vol.123 
Number (no) no.338(IT), no.339(SIP), no.340(RCS) 
Page p.204 
#Pages 1 
Date of Issue 2024-01-11 (IT, SIP, RCS) 

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