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| Presentation | 2021-11-15 09:00 [Poster Presentation] A consideration of training datasets for universal detectors of CNN-generated images Miki Tanaka, Hitoshi Kiya (Tokyo Metro. Univ.)  EA2021-32 EMM2021-59 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Recent rapid advances in convolutional neural networks (CNNs) have made manipulating and generating images easy, so synthetic media have been spread rapidly. In contrast, CNN-generated images have generated a new problem that is to make more complicated to judge images to be either real images or ones generated with CNNs. In this paper, we aim to improve a universal detector of CNN-generated images. It is difficult to train a detector by using images generated from all CNN-generation models because there are a variety of CNN-generated models. In this paper, CNN-generated models are classified in accordance with the properties that existing CNN models have, and a universal detector is then trained by using images selected from each class. In addition, from an ensemble of RGB images and spectrum images is also proposed to enhance the performance of the proposed universal detector. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | GAN / CNN / checkerboard artifact / fake image / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 247, EMM2021-59, pp. 31-36, Nov. 2021. | |
| Paper # | EMM2021-59 | |
| Date of Issue | 2021-11-08 (EA, 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 | EA2021-32 EMM2021-59 |
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| Conference Information | |
| Committee | EMM EA ASJ-H |
| Conference Date | 2021-11-15 - 2021-11-16 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | [Beginners Session] Engineering/Electro Acoustics, Content Processing, Digital Watermarking, Psychological and Physiological Acoustics, and Related Topics |
| Paper Information | |
| Registration To | EMM |
| Conference Code | 2021-11-EMM-EA-H |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | A consideration of training datasets for universal detectors of CNN-generated images |
| Sub Title (in English) | |
| Keyword(1) | GAN |
| Keyword(2) | CNN |
| Keyword(3) | checkerboard artifact |
| Keyword(4) | fake image |
| Keyword(5) | |
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| 1st Author's Name | Miki Tanaka |
| 1st Author's Affiliation | Tokyo Metropolitan University (Tokyo Metro. Univ.) |
| 2nd Author's Name | Hitoshi Kiya |
| 2nd Author's Affiliation | Tokyo Metropolitan University (Tokyo Metro. Univ.) |
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| Speaker | Author-1 |
| Date Time | 2021-11-15 09:00:00 |
| Presentation Time | 120 minutes |
| Registration for | EMM |
| Paper # | EA2021-32, EMM2021-59 |
| Volume (vol) | vol.121 |
| Number (no) | no.246(EA), no.247(EMM) |
| Page | pp.31-36 |
| #Pages | 6 |
| Date of Issue | 2021-11-08 (EA, EMM) |
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