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Paper Abstract and Keywords
Presentation 2023-03-16 15:35
Model compression by pruning of CNN based on perceptual hashes
Shota Mishina, Tetsuya Morizumi, Hirotsugu Kinoshita (Kanagawa Univ.) SITE2022-59 IA2022-82
Abstract (in Japanese) (See Japanese page) 
(in English) Message digests that identify images are indispensable for secure and convenient copyright management of digital content. Considering the processing and editing of digital content during the distribution process, a conventional cryptographic hash function is not sufficient. Conventional perceptual hashing methods are not sufficiently resistant to processing and editing, but perceptual hashing based on convolutional neural network (CNN) weight coefficients is sufficiently resistant. However, the learned models must be shared between the generator and the verifier, and reducing the size of the shared data is a problem. In this study, we focus on pruning as a method of model compression for CNN models to reduce the shared data size, and evaluate the relationship between the accuracy of perceptual hash identification and the compression ratio.
Keyword (in Japanese) (See Japanese page) 
(in English) Perceptual Hashing / Machine Learning / Pruning / Model Compression / Digital Copyright Management / / /  
Reference Info. IEICE Tech. Rep., vol. 122, no. 433, SITE2022-59, pp. 28-34, March 2023.
Paper # SITE2022-59 
Date of Issue 2023-03-08 (SITE, IA) 
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 IA SITE IPSJ-IOT  
Conference Date 2023-03-15 - 2023-03-17 
Place (in Japanese) (See Japanese page) 
Place (in English) Maebashi Institute of Technology 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Internet and Information Ethics Education, etc. 
Paper Information
Registration To SITE 
Conference Code 2023-03-IA-SITE-IOT 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Model compression by pruning of CNN based on perceptual hashes 
Sub Title (in English)  
Keyword(1) Perceptual Hashing  
Keyword(2) Machine Learning  
Keyword(3) Pruning  
Keyword(4) Model Compression  
Keyword(5) Digital Copyright Management  
Keyword(6)  
Keyword(7)  
Keyword(8)  
1st Author's Name Shota Mishina  
1st Author's Affiliation Graduate School of Kanagawa University (Kanagawa Univ.)
2nd Author's Name Tetsuya Morizumi  
2nd Author's Affiliation Kanagawa University (Kanagawa Univ.)
3rd Author's Name Hirotsugu Kinoshita  
3rd Author's Affiliation Kanagawa University (Kanagawa Univ.)
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Speaker Author-1 
Date Time 2023-03-16 15:35:00 
Presentation Time 25 minutes 
Registration for SITE 
Paper # SITE2022-59, IA2022-82 
Volume (vol) vol.122 
Number (no) no.433(SITE), no.434(IA) 
Page pp.28-34 
#Pages 7 
Date of Issue 2023-03-08 (SITE, IA) 

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