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| 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 |
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| 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 | 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 | EMM2020-72 |
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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 |
| Keyword(7) | |
| 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 | Author-1 |
| Date Time | 2021-03-04 14:45:00 |
| Presentation Time | 15 minutes |
| Registration for | EMM |
| Paper # | EMM2020-72 |
| Volume (vol) | vol.120 |
| Number (no) | no.418 |
| Page | pp.28-33 |
| #Pages | 6 |
| Date of Issue | 2021-02-25 (EMM) |
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