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Paper Abstract and Keywords
Presentation 2024-03-02 16:20
[Fellow Memorial Lecture] Application of associative memory models to watermarking models
Masaki Kawamura (Yamaguchi Univ.) EMM2023-93
Abstract (in Japanese) (See Japanese page) 
(in English) We proposed a new method called the associative watermarking method, which is an extension of the zero-watermarking method based on associative memory models. The proposed method consists of a heteroassociative model and an auto-associative model. In this paper, we show that the recurrent neural networks are as useful as the layered neural networks. We also explain the principle of pattern retrieval in the associative memory model, and show that it is possible to retrieve a memorized pattern from a degraded pattern. Thus, we show that the associative memory models have the ability to correct errors. By applying this function, we describe an idea to construct a zero watermarking method based on the associative memory models. Furthermore, the associative memory models can be analyzed by statistical neurodynamics. We show the retrieval process and the basin of attraction obtained from its macroscopic state equations. As a result, the results of theory and computer simulations are in good agreement.
Keyword (in Japanese) (See Japanese page) 
(in English) associative momory model / zero-watermarking method / recurrent neural networks / statistical neurodynamics / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 407, EMM2023-93, pp. 23-27, March 2024.
Paper # EMM2023-93 
Date of Issue 2024-02-24 (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)
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Conference Information
Committee EMM  
Conference Date 2024-03-02 - 2024-03-03 
Place (in Japanese) (See Japanese page) 
Place (in English) Day1:JEJU TECHNOPARK, Day2:JEJU Business Agency 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To EMM 
Conference Code 2024-03-EMM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Application of associative memory models to watermarking models 
Sub Title (in English)  
Keyword(1) associative momory model  
Keyword(2) zero-watermarking method  
Keyword(3) recurrent neural networks  
Keyword(4) statistical neurodynamics  
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1st Author's Name Masaki Kawamura  
1st Author's Affiliation Yamaguchi University (Yamaguchi Univ.)
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Speaker Author-1 
Date Time 2024-03-02 16:20:00 
Presentation Time 50 minutes 
Registration for EMM 
Paper # EMM2023-93 
Volume (vol) vol.123 
Number (no) no.407 
Page pp.23-27 
#Pages 5 
Date of Issue 2024-02-24 (EMM) 

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