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Paper Abstract and Keywords
Presentation 2026-03-05 14:50
[Invited Talk] Utilizing Adversarial Examples -- Transforming DNN Vulnerabilities into Practical Utility --
Mitsuji muneyasu (Kansai Univ.) SIS2025-63
Abstract (in Japanese) (See Japanese page) 
(in English) This presentation discusses Adversarial Examples (AE) in deep learning and their applications in medical image processing and digital watermarking. AEs are designed to induce misclassification in Deep Neural Networks (DNNs) by adding subtle perturbations to input data that are imperceptible to the human eye. In this context, rather than treating AEs merely as a threat, we introduce an approach that leverages them to improve DNN performance and enhance security. First, after providing an overview of AEs, we demonstrate the utility of adversarial training—using AEs in reverse—for medical images. Specifically, we discuss its application to the task of detecting calcification regions, an indicator of arteriosclerosis, in dental panoramic radiographs. Next, we introduce an attempt to apply AEs to digital watermarking. In this section, we describe a method for enhancing information secrecy by treating the perturbations that cause a model to misclassify an image into a specific class as "information embedding." Throughout this presentation, we aim to demonstrate the potential to transform the vulnerabilities of DNNs into practical utility.
Keyword (in Japanese) (See Japanese page) 
(in English) deep learning / adversarial examples / adversarial perturbation / medical image processing / carotid artery calcification / digital image watermarking / secrecy /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 393, SIS2025-63, pp. 33-33, March 2026.
Paper # SIS2025-63 
Date of Issue 2026-02-26 (SIS) 
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 SIS2025-63

Conference Information
Committee SIS  
Conference Date 2026-03-05 - 2026-03-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Ohkubo Campus, Saitama University 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To SIS 
Conference Code 2026-03-SIS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Utilizing Adversarial Examples 
Sub Title (in English) Transforming DNN Vulnerabilities into Practical Utility 
Keyword(1) deep learning  
Keyword(2) adversarial examples  
Keyword(3) adversarial perturbation  
Keyword(4) medical image processing  
Keyword(5) carotid artery calcification  
Keyword(6) digital image watermarking  
Keyword(7) secrecy  
Keyword(8)  
1st Author's Name Mitsuji muneyasu  
1st Author's Affiliation Kansai University (Kansai Univ.)
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Speaker Author-1 
Date Time 2026-03-05 14:50:00 
Presentation Time 60 minutes 
Registration for SIS 
Paper # SIS2025-63 
Volume (vol) vol.125 
Number (no) no.393 
Page p.33 
#Pages 1 
Date of Issue 2026-02-26 (SIS) 

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