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Paper Abstract and Keywords
Presentation 2021-11-15 09:00
[Poster Presentation] A Study on Convergency of DNN Watermarking without Embedding Loss Function
Takuro Tanaka, Tatsuya Yasui, Minoru Kuribayashi, Nobuo Funabiki (Okayama Univ.) EA2021-35 EMM2021-62
Abstract (in Japanese) (See Japanese page) 
(in English) Intellectual Property Rights protection related to Deep Neural Networks is an important issue due to the high cost of training DNNs and the widespread use of DNNs in society.
We have proposed a method to embed the watermark information directly into the frequency components of some weight parameters without using embedding loss.
It has been experimentally shown that the embedding operation has little impact on the convergence of the training.
In this paper, we quantitatively evaluate the impact of embedding on the convergence of the training.
We also implement the embedding operation on the ResNet in order to check the versatility of the proposed method.
Keyword (in Japanese) (See Japanese page) 
(in English) watermarking / DM-QIM / DNN model / fine-tuning / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 247, EMM2021-62, pp. 49-54, Nov. 2021.
Paper # EMM2021-62 
Date of Issue 2021-11-08 (EA, EMM) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
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 EA2021-35 EMM2021-62

Conference Information
Committee EMM EA ASJ-H  
Conference Date 2021-11-15 - 2021-11-16 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) [Beginners Session] Engineering/Electro Acoustics, Content Processing, Digital Watermarking, Psychological and Physiological Acoustics, and Related Topics 
Paper Information
Registration To EMM 
Conference Code 2021-11-EMM-EA-H 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Study on Convergency of DNN Watermarking without Embedding Loss Function 
Sub Title (in English)  
Keyword(1) watermarking  
Keyword(2) DM-QIM  
Keyword(3) DNN model  
Keyword(4) fine-tuning  
1st Author's Name Takuro Tanaka  
1st Author's Affiliation Okayama University (Okayama Univ.)
2nd Author's Name Tatsuya Yasui  
2nd Author's Affiliation Okayama University (Okayama Univ.)
3rd Author's Name Minoru Kuribayashi  
3rd Author's Affiliation Okayama University (Okayama Univ.)
4th Author's Name Nobuo Funabiki  
4th Author's Affiliation Okayama University (Okayama Univ.)
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Speaker Author-1 
Date Time 2021-11-15 09:00:00 
Presentation Time 120 minutes 
Registration for EMM 
Paper # EA2021-35, EMM2021-62 
Volume (vol) vol.121 
Number (no) no.246(EA), no.247(EMM) 
Page pp.49-54 
Date of Issue 2021-11-08 (EA, EMM) 

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