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Paper Abstract and Keywords
Presentation 2024-11-24 13:30
[Poster Presentation] Model Extraction Attack and Its Defense on Diffusion-Based Unconditional Image Generators
Hayato Shoji, Kazuaki Nakamura (TUS) EA2024-51 EMM2024-90
Abstract (in Japanese) (See Japanese page) 
(in English) Recently, the threat of cyber attacks against machine learning models has been increasing, one of whose typical examples is Model Extraction Attack (MEA). MEA is an attack of constructing an illegal clone of a victim model exploiting its outputs. Most existing work focuses on image recognition models as a target of MEA while image generation model attracts less attention. In this paper, we first evaluate the feasibility of MEA against unconditional DDIM, which is a diffusion-based image generation model. Then, we propose a method for detecting clone models as a countermeasure for MEA against unconditional DDIM. Specifically, we add a few out-of-distribution (OOD) images to the victim model's training dataset to make the model occasionally output OOD images. This property is expected to be inherited by clone models and therefore is used as a clue for detecting them. Experimental results show that MEA can also be conducted on image generation models while the proposed method successfully forces the clone models to have the same property as the victim model.
Keyword (in Japanese) (See Japanese page) 
(in English) Model Extraction Attack / clone model detection / image generation model / diffusion model / unconditional DDIM / / /  
Reference Info. IEICE Tech. Rep., vol. 124, no. 272, EMM2024-90, pp. 45-50, Nov. 2024.
Paper # EMM2024-90 
Date of Issue 2024-11-17 (EA, 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 EA2024-51 EMM2024-90

Conference Information
Committee EA EMM ASJ-H  
Conference Date 2024-11-24 - 2024-11-25 
Place (in Japanese) (See Japanese page) 
Place (in English) SARAI Wellness House 
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 2024-11-EA-EMM-H 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Model Extraction Attack and Its Defense on Diffusion-Based Unconditional Image Generators 
Sub Title (in English)  
Keyword(1) Model Extraction Attack  
Keyword(2) clone model detection  
Keyword(3) image generation model  
Keyword(4) diffusion model  
Keyword(5) unconditional DDIM  
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1st Author's Name Hayato Shoji  
1st Author's Affiliation Tokyo University of Science (TUS)
2nd Author's Name Kazuaki Nakamura  
2nd Author's Affiliation Tokyo University of Science (TUS)
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Speaker Author-1 
Date Time 2024-11-24 13:30:00 
Presentation Time 150 minutes 
Registration for EMM 
Paper # EA2024-51, EMM2024-90 
Volume (vol) vol.124 
Number (no) no.271(EA), no.272(EMM) 
Page pp.45-50 
#Pages 6 
Date of Issue 2024-11-17 (EA, EMM) 

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