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| Presentation | 2020-03-05 16:45 [Poster Presentation] Video Forgery Detection Using Generative Adversarial Networks Shoken Ohshiro (Osaka Univ.), Kazuhiro Kono (Kansai Univ.), Noboru Babaguchi (Osaka Univ.)  EMM2019-122 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | The purpose of our work is to detect the regions of tampered objects in the spatial domain of videos by passive approaches. The videos include dynamic scenes like camera shake. We regard the task of tampering detection as the task of the generation of predict maps of the tampered regions. Therefore, we adopt a Generative Adversarial Network (GAN) for video forgery detection. The generator of a GAN has an Encoder-Decoder structure, and the discriminator of a GAN has a Two-Stream Network. Our proposed model achieved Area under the Precision-Recall Curve (AUC of PR curve) 0.57 and higher accuracy than existing methods. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Video Forgery Detection / Dynamic Scene / Object Modification / Generative Adversarial Network / Two-Stream Network / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 119, no. 463, EMM2019-122, pp. 107-112, March 2020. | |
| Paper # | EMM2019-122 | |
| Date of Issue | 2020-02-27 (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 | EMM2019-122 |
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| Conference Information | |
| Committee | EMM |
| Conference Date | 2020-03-05 - 2020-03-06 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| 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 | 2020-03-EMM |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | 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) | Two-Stream Network |
| 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 (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 | 2020-03-05 16:45:00 |
| Presentation Time | 60 minutes |
| Registration for | EMM |
| Paper # | EMM2019-122 |
| Volume (vol) | vol.119 |
| Number (no) | no.463 |
| Page | pp.107-112 |
| #Pages | 6 |
| Date of Issue | 2020-02-27 (EMM) |
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