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| Presentation | 2018-11-21 13:30 [Poster Presentation] Video Forgery Detection Using Spatio-Temporal Convolutional Neural Network Shoken Ohshiro (Osaka Univ), Kazuhiro Kono (Kainsai Univ), Noboru Babaguchi (Osaka Univ)  EA2018-71 EMM2018-71 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | It is easy to tamper with videos due to the improvement of video editing technology.We need to develop forgery detection systems in order to guarantee the integrity of the videos.The purpose of our work is to detect tampering in the spatial domain for the videos which include dynamic scenes like non-stationary scenes.In this paper, we develop a video forgery detection system using 3D Convolutional Neural Network (CNN).Unlike 2D CNN, the filters in 3D CNN can extract features from both spatial and temporal aspects of the videos.We report the experimental results in our proposed system and the existing system using Convolutional Long Short-Term Memory. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Video Analysis / Video Forgery Detection / Passive Approach / 3D CNN / Object Modification / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 118, no. 313, EMM2018-71, pp. 49-52, Nov. 2018. | |
| Paper # | EMM2018-71 | |
| Date of Issue | 2018-11-14 (EA, 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 | EA2018-71 EMM2018-71 |
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| Conference Information | |
| Committee | EA ASJ-H EMM IPSJ-MUS |
| Conference Date | 2018-11-21 - 2018-11-22 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Hotel Koshuen |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | [Beginners Session] Engineering/Electro Acoustics, Psychological and Physiological Acoustics, Music and Computer, Content Processing, Digital Watermarking, and Related Topics |
| Paper Information | |
| Registration To | EMM |
| Conference Code | 2018-11-EA-H-EMM-MUS |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Video Forgery Detection Using Spatio-Temporal Convolutional Neural Network |
| Sub Title (in English) | |
| Keyword(1) | Video Analysis |
| Keyword(2) | Video Forgery Detection |
| Keyword(3) | Passive Approach |
| Keyword(4) | 3D CNN |
| Keyword(5) | Object Modification |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Shoken Ohshiro |
| 1st Author's Affiliation | Osaka University (Osaka Univ) |
| 2nd Author's Name | Kazuhiro Kono |
| 2nd Author's Affiliation | Kansai University (Kainsai Univ) |
| 3rd Author's Name | Noboru Babaguchi |
| 3rd Author's Affiliation | Osaka University (Osaka Univ) |
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| Speaker | Author-1 |
| Date Time | 2018-11-21 13:30:00 |
| Presentation Time | 150 minutes |
| Registration for | EMM |
| Paper # | EA2018-71, EMM2018-71 |
| Volume (vol) | vol.118 |
| Number (no) | no.312(EA), no.313(EMM) |
| Page | pp.49-52 |
| #Pages | 4 |
| Date of Issue | 2018-11-14 (EA, EMM) |
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