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| Presentation | 2022-03-07 16:35 Access Control of Vision Transformer models without performance degradation Ryota Iijima, Yuma Kinoshita, Hitoshi Kiya (Tokyo Metro. Univ.)  EMM2021-104 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In this paper, we propose a novel access control method to protect trained models from unauthorized access. Conventional access control methods cause some performance degradation, and need additional calculations to offer an access control function to models in general. In addition, models have to be trained with specially prepared data such as encrypted images. In contrast, the proposed method does not require any specially prepared data and network modification, and does not cause any performance degradation. The proposed method focuses on the patching process of the vision transformer (ViT) and the structure of the position embedding. We point out that the trained model can be directly encrypted on the bases of the architecture of ViT, and the encrypted models are effective in access control. In an experiment, the effectiveness of the proposed method is evaluated in terms of performance degradation and access control in an image classification task under the use of the CIFAR-10 dataset. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Vision Transformer / access control / / / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 121, no. 417, EMM2021-104, pp. 70-75, March 2022. | |
| Paper # | EMM2021-104 | |
| Date of Issue | 2022-02-28 (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 | EMM2021-104 |
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| Conference Information | |
| Committee | EMM |
| Conference Date | 2022-03-07 - 2022-03-08 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | (Primary: Online, Secondary: On-site) |
| 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 | 2022-03-EMM |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Access Control of Vision Transformer models without performance degradation |
| Sub Title (in English) | |
| Keyword(1) | Vision Transformer |
| Keyword(2) | access control |
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| 1st Author's Name | Ryota Iijima |
| 1st Author's Affiliation | Tokyo Metropolitan University (Tokyo Metro. Univ.) |
| 2nd Author's Name | Yuma Kinoshita |
| 2nd Author's Affiliation | Tokyo Metropolitan University (Tokyo Metro. Univ.) |
| 3rd Author's Name | Hitoshi Kiya |
| 3rd Author's Affiliation | Tokyo Metropolitan University (Tokyo Metro. Univ.) |
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| Speaker | Author-1 |
| Date Time | 2022-03-07 16:35:00 |
| Presentation Time | 25 minutes |
| Registration for | EMM |
| Paper # | EMM2021-104 |
| Volume (vol) | vol.121 |
| Number (no) | no.417 |
| Page | pp.70-75 |
| #Pages | 6 |
| Date of Issue | 2022-02-28 (EMM) |
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