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Paper Abstract and Keywords
Presentation 2025-05-29 16:15
A privacy-preserving semantic segmentation method that enables model training on cloud servers
Homare Sueyoshi, Kouki Horio, Kiyoshi Nishikawa, Hitoshi Kiya (TMU) IT2025-7 EMM2025-7
Abstract (in Japanese) (See Japanese page) 
(in English) We propose a privacy-preserving semantic segmentation method that can apply perceptual encryption to images used for model learning in addition to test images. This feature makes it possible to protect visual information, which was previously limited to test images, including images used for model training on cloud servers whose reliability cannot be assumed. In addition, it provides almost the same accuracy as that of unencrypted images. The above performance is achieved by using a domain adaptation technique on the embedding structure of the Vision Transformer (ViT). In experiments, the effectiveness of the proposed method is confirmed in terms of the accuracy of semantic segmentation under the use of Segmentation Transformer (SETR) with ViT as a model as an example.
Keyword (in Japanese) (See Japanese page) 
(in English) Semantic Segmentation / Vision Transformer / Image Encryption / Privacy-preserving / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 38, EMM2025-7, pp. 37-42, May 2025.
Paper # EMM2025-7 
Date of Issue 2025-05-22 (IT, EMM) 
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 IT2025-7 EMM2025-7

Conference Information
Committee IT EMM  
Conference Date 2025-05-29 - 2025-05-30 
Place (in Japanese) (See Japanese page) 
Place (in English) Nagasaki University 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Information Security, Information Theory, Information Hiding, etc., Freshman session 
Paper Information
Registration To EMM 
Conference Code 2025-05-IT-EMM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A privacy-preserving semantic segmentation method that enables model training on cloud servers 
Sub Title (in English)  
Keyword(1) Semantic Segmentation  
Keyword(2) Vision Transformer  
Keyword(3) Image Encryption  
Keyword(4) Privacy-preserving  
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1st Author's Name Homare Sueyoshi  
1st Author's Affiliation Tokyo Metropolitan University (TMU)
2nd Author's Name Kouki Horio  
2nd Author's Affiliation Tokyo Metropolitan University (TMU)
3rd Author's Name Kiyoshi Nishikawa  
3rd Author's Affiliation Tokyo Metropolitan University (TMU)
4th Author's Name Hitoshi Kiya  
4th Author's Affiliation Tokyo Metropolitan University (TMU)
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Speaker Author-1 
Date Time 2025-05-29 16:15:00 
Presentation Time 25 minutes 
Registration for EMM 
Paper # IT2025-7, EMM2025-7 
Volume (vol) vol.125 
Number (no) no.37(IT), no.38(EMM) 
Page pp.37-42 
#Pages 6 
Date of Issue 2025-05-22 (IT, EMM) 

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