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| Presentation | 2021-11-15 09:00 [Poster Presentation] A Study on Convergency of DNN Watermarking without Embedding Loss Function Takuro Tanaka, Tatsuya Yasui, Minoru Kuribayashi, Nobuo Funabiki (Okayama Univ.)  EA2021-35 EMM2021-62 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Intellectual Property Rights protection related to Deep Neural Networks is an important issue due to the high cost of training DNNs and the widespread use of DNNs in society. We have proposed a method to embed the watermark information directly into the frequency components of some weight parameters without using embedding loss. It has been experimentally shown that the embedding operation has little impact on the convergence of the training. In this paper, we quantitatively evaluate the impact of embedding on the convergence of the training. We also implement the embedding operation on the ResNet in order to check the versatility of the proposed method. |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | watermarking / DM-QIM / DNN model / fine-tuning / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 247, EMM2021-62, pp. 49-54, Nov. 2021. | |
| Paper # | EMM2021-62 | |
| Date of Issue | 2021-11-08 (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 | EA2021-35 EMM2021-62 |
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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 Study on Convergency of DNN Watermarking without Embedding Loss Function |
| Sub Title (in English) | |
| Keyword(1) | watermarking |
| Keyword(2) | DM-QIM |
| Keyword(3) | DNN model |
| Keyword(4) | fine-tuning |
| Keyword(5) | |
| Keyword(6) | |
| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Takuro Tanaka |
| 1st Author's Affiliation | Okayama University (Okayama Univ.) |
| 2nd Author's Name | Tatsuya Yasui |
| 2nd Author's Affiliation | Okayama University (Okayama Univ.) |
| 3rd Author's Name | Minoru Kuribayashi |
| 3rd Author's Affiliation | Okayama University (Okayama Univ.) |
| 4th Author's Name | Nobuo Funabiki |
| 4th Author's Affiliation | Okayama University (Okayama Univ.) |
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| Speaker | Author-1 |
| Date Time | 2021-11-15 09:00:00 |
| Presentation Time | 120 minutes |
| Registration for | EMM |
| Paper # | EA2021-35, EMM2021-62 |
| Volume (vol) | vol.121 |
| Number (no) | no.246(EA), no.247(EMM) |
| Page | pp.49-54 |
| #Pages | 6 |
| Date of Issue | 2021-11-08 (EA, EMM) |
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