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| Paper Abstract and Keywords | ||
| Presentation | 2018-03-05 15:45 [Poster Presentation] What kind of embedding methods do neural networks learn? Ippei Hamamoto, Masaki Kawamura (Yamaguchi Univ.)  EMM2017-83 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | We proposed an embedder, i.e., an embedding method using a layered neural network. The discrete cosine transform (DCT) coefficients of stego-images and watermarks are represented in stego-layer (3rd layer) and output layer (5th layer), respectively. The embedding method was evaluated both its robustness against JPEG compression and image quality for the stego-images. Although it was shown that it was the blind embedding method which could embed and extract watermarks from unlearned images, it was not clear that the embedder neural network could learn the watermarks in the form of either distributed or sparse representations. Therefore, we make it clear whether watermarks are represented widely or sparsely in the stego-layer of the trained neural network. As a result, while the embedder could acquire the distributed representation in the early stage of learning, the representation would change to the sparse one in the late stage. Since the embedder will be trained in accordance with the evaluation function which measures by both the image quality of the stego-image and errors of the watermarks, the sparse representation has advantage for the image quality. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | digital watermark / neural network / distributed representation / sparse representation / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 117, no. 476, EMM2017-83, pp. 31-36, March 2018. | |
| Paper # | EMM2017-83 | |
| 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 | EMM2017-83 |
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| 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) | What kind of embedding methods do neural networks learn? |
| Sub Title (in English) | |
| Keyword(1) | digital watermark |
| Keyword(2) | neural network |
| Keyword(3) | distributed representation |
| Keyword(4) | sparse representation |
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| 1st Author's Name | Ippei Hamamoto |
| 1st Author's Affiliation | Yamaguchi University (Yamaguchi Univ.) |
| 2nd Author's Name | Masaki Kawamura |
| 2nd Author's Affiliation | Yamaguchi University (Yamaguchi Univ.) |
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| Speaker | Author-1 |
| Date Time | 2018-03-05 15:45:00 |
| Presentation Time | 60 minutes |
| Registration for | EMM |
| Paper # | EMM2017-83 |
| Volume (vol) | vol.117 |
| Number (no) | no.476 |
| Page | pp.31-36 |
| #Pages | 6 |
| Date of Issue | 2018-02-26 (EMM) |
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