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Paper Abstract and Keywords
Presentation 2018-03-05 14:35
[Poster Presentation] Video Forgery Detection Considering Spatio-Temporal Consistency
Takaaki Yoshida (Osaka Univ.), Kazuhiro Kono (Kansai Univ.), Noboru Babaguchi (Osaka Univ.) EMM2017-81
Abstract (in Japanese) (See Japanese page) 
(in English) This paper proposes a video forgery detection method in dynamic scenes such as dynamic background or dynamic perspectives.
In order to adapt to dynamic scenes, we use Convolutional Neural Network and Recurrent Neural Network together.
This enables as to consider spatio-temporal consistency of videos. We also construct new video forgery databases for object modification as well as object removal.
Our proposed method using Convolutional LSTM achieved Area-Under-Curve (AUC) 0.970 and Equal-Error-Rate (EER) 0.078 on the object addition database
and AUC 0.872 and EER 0.219 on the object modification database.
Keyword (in Japanese) (See Japanese page) 
(in English) Video Forgery Detection / Dynamic scene / Convolutional LSTM / CNN / RNN / Modification Dataset / /  
Reference Info. IEICE Tech. Rep., vol. 117, no. 476, EMM2017-81, pp. 23-28, March 2018.
Paper # EMM2017-81 
Date of Issue 2018-02-26 (EMM) 
ISSN Print edition: ISSN 0913-5685    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 EMM2017-81

Conference Information
Committee EMM  
Conference Date 2018-03-05 - 2018-03-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Naze Community Center (Amami-Shi, Kagoshima) 
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 2018-03-EMM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Video Forgery Detection Considering Spatio-Temporal Consistency 
Sub Title (in English)  
Keyword(1) Video Forgery Detection  
Keyword(2) Dynamic scene  
Keyword(3) Convolutional LSTM  
Keyword(4) CNN  
Keyword(5) RNN  
Keyword(6) Modification Dataset  
Keyword(7)  
Keyword(8)  
1st Author's Name Takaaki Yoshida  
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 Author-1 
Date Time 2018-03-05 14:35:00 
Presentation Time 60 minutes 
Registration for EMM 
Paper # EMM2017-81 
Volume (vol) vol.117 
Number (no) no.476 
Page pp.23-28 
#Pages 6 
Date of Issue 2018-02-26 (EMM) 

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