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Paper Abstract and Keywords
Presentation 2021-03-04 14:45
[Poster Presentation] Improvement of Video Forgery Detection Using Generative Adversarial Networks
Yutaro Osako (Osaka Univ.), Kazuhiro Kono (Kansai Univ.), Noboru Babaguchi (Osaka Univ.) EMM2020-72
Abstract (in Japanese) (See Japanese page) 
(in English) Our work aims to detect tampered objects in the spatial domain of videos with high accuracy. We target videos, including dynamic scenes like camera shake. Our group proposed a video forgery detection using generative adversarial networks (GAN) in the past. In this paper, we describe and improve three issues in the detection system: the ratio of tampered videos and original videos in datasets, two kinds of loss functions, and the generator's network in GAN. We also report that our improved forgery detection system based on GAN achieved higher accuracy than the related detection system.
Keyword (in Japanese) (See Japanese page) 
(in English) Video Forgery Detection / Dynamic Scene / Object Modification / Generative Adversarial Network / Loss function / Generator / /  
Reference Info. IEICE Tech. Rep., vol. 120, no. 418, EMM2020-72, pp. 28-33, March 2021.
Paper # EMM2020-72 
Date of Issue 2021-02-25 (EMM) 
ISSN Print edition: ISSN 0913-5685  Online edition: ISSN 2432-6380
Copyright
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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 2021-03-04 - 2021-03-05 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image and Sound Quality, Metrics for Perception and Recognition, Human Auditory and Visual System, etc. 
Paper Information
Registration To EMM 
Conference Code 2021-03-EMM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Improvement of Video Forgery Detection Using Generative Adversarial Networks 
Sub Title (in English)  
Keyword(1) Video Forgery Detection  
Keyword(2) Dynamic Scene  
Keyword(3) Object Modification  
Keyword(4) Generative Adversarial Network  
Keyword(5) Loss function  
Keyword(6) Generator  
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Keyword(8)  
1st Author's Name Yutaro Osako  
1st Author's Affiliation Osaka University (Osaka Univ.)
2nd Author's Name Kazuhiro Kono  
2nd Author's Affiliation Kansai University (Kansai Univ.)
3rd Author's Name Noboru Babaguchi  
3rd Author's Affiliation Osaka University (Osaka Univ.)
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Speaker
Date Time 2021-03-04 14:45:00 
Presentation Time 15 
Registration for EMM 
Paper # IEICE-EMM2020-72 
Volume (vol) IEICE-120 
Number (no) no.418 
Page pp.28-33 
#Pages IEICE-6 
Date of Issue IEICE-EMM-2021-02-25 


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