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Paper Abstract and Keywords
Presentation 2026-03-16 11:15
Image Protection against Personalized Latent Diffusion Models via Adversarial Attacks
Tsubasa Aihara, Koki Shimizu, Terumasa Aoki (TUT) IMQ2025-34 IE2025-111 MVE2025-41
Abstract (in Japanese) (See Japanese page) 
(in English) Image generation technology using generative AI has evolved rapidly, that is the emergence of large-scale Latent Diffusion Models, such as Stable Diffusion. Traditionally, these image generation technologies have had difficulty generating a specific person. However, DreamBooth has made it possible to generate a specific person from a small number of images of that person. These technologies require few images to generate, which raises concerns about potential malicious use, such as deepfakes. In this paper, we propose a novel image protection method that disrupts generative AI training. Specifically, by applying Adversarial Perturbations that simultaneously optimize structural disruption and semantic divergence in the Latent space, we successfully disrupted training from both structural and semantic perspectives while maintaining visual image quality. This significantly degraded both the quality of generated images and the fidelity of person identity.
Keyword (in Japanese) (See Japanese page) 
(in English) Latent Diffusion Models / Stable Diffusion / DreamBooth / Personalized fine tuning / Adversarial Attacks / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 410, IE2025-111, pp. 81-86, March 2026.
Paper # IE2025-111 
Date of Issue 2026-03-09 (IMQ, IE, MVE) 
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 IMQ2025-34 IE2025-111 MVE2025-41

Conference Information
Committee CQ MVE IMQ IE  
Conference Date 2026-03-16 - 2026-03-18 
Place (in Japanese) (See Japanese page) 
Place (in English) Okinawa-Sangyoushien-Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To IE 
Conference Code 2026-03-CQ-MVE-IMQ-IE 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Image Protection against Personalized Latent Diffusion Models via Adversarial Attacks 
Sub Title (in English)  
Keyword(1) Latent Diffusion Models  
Keyword(2) Stable Diffusion  
Keyword(3) DreamBooth  
Keyword(4) Personalized fine tuning  
Keyword(5) Adversarial Attacks  
Keyword(6)  
Keyword(7)  
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1st Author's Name Tsubasa Aihara  
1st Author's Affiliation Tokyo University of Technology (TUT)
2nd Author's Name Koki Shimizu  
2nd Author's Affiliation Tokyo University of Technology (TUT)
3rd Author's Name Terumasa Aoki  
3rd Author's Affiliation Tokyo University of Technology (TUT)
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Speaker Author-1 
Date Time 2026-03-16 11:15:00 
Presentation Time 20 minutes 
Registration for IE 
Paper # IMQ2025-34, IE2025-111, MVE2025-41 
Volume (vol) vol.125 
Number (no) no.408(IMQ), no.410(IE), no.411(MVE) 
Page pp.81-86 
#Pages 6 
Date of Issue 2026-03-09 (IMQ, IE, MVE) 

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