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| Presentation | 2023-09-12 15:35 Variational Autoencoder Oriented Protection for Intellectual Property Ryo Kumagai, Shu Takemoto, Yusuke Nozaki, Masaya Yoshikawa (Meijo Univ.)  AI2023-31 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | In recent years, generative AI, which generates images based on instructions in natural language, has developed rapidly and is expected to be widely applied. However, many images on the Internet are used without permissions. In addition, there is a risk of generating fake images based on real images and being abused to spread false information. In this study, we propose a method to generate adversarial example samples (AE) based on a variational autoencoder (VAE) of a latent diffusion model (LDM). By adding an imperceptible adversarial perturbation to the input image, we degrade the quality of the image generated by the model and prevent unauthorized use of the protected content. In the evaluation experiment, AE is used as input for an image-to-image task, and the effect of adversarial perturbation on the output of LDM is evaluated. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | artificial intelligence / latent diffusion models / variational autoencoder / adversarial examples / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 123, no. 190, AI2023-31, pp. 180-186, Sept. 2023. | |
| Paper # | AI2023-31 | |
| Date of Issue | 2023-09-05 (AI) | |
| 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 | AI2023-31 |
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| Conference Information | |
| Committee | AI |
| Conference Date | 2023-09-12 - 2023-09-13 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | AI |
| Conference Code | 2023-09-AI |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Variational Autoencoder Oriented Protection for Intellectual Property |
| Sub Title (in English) | |
| Keyword(1) | artificial intelligence |
| Keyword(2) | latent diffusion models |
| Keyword(3) | variational autoencoder |
| Keyword(4) | adversarial examples |
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| 1st Author's Name | Ryo Kumagai |
| 1st Author's Affiliation | Meijo University (Meijo Univ.) |
| 2nd Author's Name | Shu Takemoto |
| 2nd Author's Affiliation | Meijo University (Meijo Univ.) |
| 3rd Author's Name | Yusuke Nozaki |
| 3rd Author's Affiliation | Meijo University (Meijo Univ.) |
| 4th Author's Name | Masaya Yoshikawa |
| 4th Author's Affiliation | Meijo University (Meijo Univ.) |
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| Speaker | Author-1 |
| Date Time | 2023-09-12 15:35:00 |
| Presentation Time | 20 minutes |
| Registration for | AI |
| Paper # | AI2023-31 |
| Volume (vol) | vol.123 |
| Number (no) | no.190 |
| Page | pp.180-186 |
| #Pages | 7 |
| Date of Issue | 2023-09-05 (AI) |
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