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Paper Abstract and Keywords
Presentation 2026-01-19 15:40
Pruning-robust Associative Watermarking Method for CNN protection
Keiichi Mori, Masaki Kawamura (Yamaguchi Univ.) EMM2025-107
Abstract (in Japanese) (See Japanese page) 
(in English) Deep learning models, particularly convolutional neural networks (CNNs), are considered critical intellectual property due to their enormous training costs, making protection against unauthorized use essential. To address this issue, we propose a CNN protection method that uses associative watermarking, a zero-bit watermarking technique. This method protects the model while maintaining its original recognition performance because the watermark is not directly embedded into the model. Furthermore, it has demonstrated high robustness
against common attacks due to its error correction capability.
However, its robustness against pruning has not yet been examined.
Pruning is an acceptable operation for optimizing resources.
To address pruning attacks, this study introduces a new feature extraction method that is less susceptible to them.
Specifically, we suppress pruning-induced watermark errors by creating features from virtually pruned weights. Evaluation experiments demonstrated extremely high robustness. We achieved error-free watermark detection by masking 95%
of the weights prior to feature extraction, even under severe pruning attacks where 95%
of the weights were removed.
Keyword (in Japanese) (See Japanese page) 
(in English) Zero-bit watermarking / Associative watermarking method / Convolutional Neural Network / Pruning / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 317, EMM2025-107, pp. 37-42, Jan. 2026.
Paper # EMM2025-107 
Date of Issue 2026-01-12 (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 EMM2025-107

Conference Information
Committee EMM  
Conference Date 2026-01-19 - 2026-01-20 
Place (in Japanese) (See Japanese page) 
Place (in English) Tohoku Univ. 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Sense of Presence, Universal Media, Digital Entertainment, etc. 
Paper Information
Registration To EMM 
Conference Code 2026-01-EMM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Pruning-robust Associative Watermarking Method for CNN protection 
Sub Title (in English)  
Keyword(1) Zero-bit watermarking  
Keyword(2) Associative watermarking method  
Keyword(3) Convolutional Neural Network  
Keyword(4) Pruning  
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1st Author's Name Keiichi Mori  
1st Author's Affiliation Yamaguchi University (Yamaguchi Univ.)
2nd Author's Name Masaki Kawamura  
2nd Author's Affiliation Yamaguchi University (Yamaguchi Univ.)
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Speaker Author-1 
Date Time 2026-01-19 15:40:00 
Presentation Time 25 minutes 
Registration for EMM 
Paper # EMM2025-107 
Volume (vol) vol.125 
Number (no) no.317 
Page pp.37-42 
#Pages 6 
Date of Issue 2026-01-12 (EMM) 

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