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Paper Abstract and Keywords
Presentation 2025-03-05 13:15
[Poster Presentation] Experimental evaluation of deep neural network generation management using watermarking
Ryu Furukawa, Shigeyuki Sakazawa (OIT) EMM2024-117
Abstract (in Japanese) (See Japanese page) 
(in English) In recent years, the mainstream of deep learning model development has been to generate derived models from original models trained on large datasets. Moreover, the original models are publicly available as open source resources. In this context, the reliability of the derived models depends on the reliability of the original models. To solve this problem, we use the deep learning model watermarking technique, which has been studied for the purpose of preventing unauthorized use, to implement generation management. Generation management refers to the management of copyright information to identify both original and derived model developers when a derived model is created by legitimate means. We propose two methods that embed multiple watermarks sequentially in separate weight parameters, and verify the interference between watermarks for original and derived models and their effects on the image classification task. The results show that both methods can embed and detect watermarks while avoiding severe impact on the task. In particular, Method 1, which uses more weight parameters for watermark embedding, was found to have less negative impact on the model.
Keyword (in Japanese) (See Japanese page) 
(in English) deep learning modeｌ / watermark / generation management / / / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 414, EMM2024-117, pp. 1-3, March 2025.
Paper # EMM2024-117 
Date of Issue 2025-02-26 (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 EMM2024-117

Conference Information
Committee EMM  
Conference Date 2025-03-05 - 2025-03-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawaken Seinenkaikan 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To EMM 
Conference Code 2025-03-EMM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Experimental evaluation of deep neural network generation management using watermarking 
Sub Title (in English)  
Keyword(1) deep learning modeｌ  
Keyword(2) watermark  
Keyword(3) generation management  
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1st Author's Name Ryu Furukawa  
1st Author's Affiliation Osaka Institute of Technology (OIT)
2nd Author's Name Shigeyuki Sakazawa  
2nd Author's Affiliation Osaka Institute of Technology (OIT)
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Speaker Author-1 
Date Time 2025-03-05 13:15:00 
Presentation Time 60 minutes 
Registration for EMM 
Paper # EMM2024-117 
Volume (vol) vol.124 
Number (no) no.414 
Page pp.1-3 
#Pages 3 
Date of Issue 2025-02-26 (EMM) 

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