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Paper Abstract and Keywords
Presentation 2026-07-14 15:40
LoRA-ViT in key-based domain adaptation for learnable image encryption
Haiwei Lin, Shoko Imaizumi (Chiba Univ.), Hitoshi Kiya (TMU) ISEC2026-44 SITE2026-40 BioX2026-67 HWS2026-40 ICSS2026-59 EMM2026-51
Abstract (in Japanese) (See Japanese page) 
(in English) Learnable image encryption (LIE) protects image privacy by hiding visual content from human perception while keeping the protected images learnable by deep learning models. This is achieved by applying reversible transformations controlled by a secret key to generate encrypted images for training and inference. However, most LIE methods use a fixed key for both training and inference. The recognition performance drops when query images are encrypted with a key different from that used for training images. To support images encrypted with a new key, the model typically requires full fine-tuning, where all parameters are updated. This makes LIE computationally expensive and inflexible when different data owners use different keys. To address this problem, we propose a novel LIE method that enables efficient adaptation to images encrypted with a new key. Motivated by parameter-efficient fine-tuning based on low-rank adaptation (LoRA), our method freezes most model parameters and trains only lightweight key-specific components to avoid resource-intensive full fine-tuning. This design enables the model to adapt to key changes by switching only the corresponding key-specific components.
Experimental results show that our method achieves performance comparable to that of previous methods while using substantially fewer trainable parameters.
Keyword (in Japanese) (See Japanese page) 
(in English) privacy-preserving image classification / low-rank adaptation / image encryption / vision transformers / / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 112, EMM2026-51, pp. 212-217, July 2026.
Paper # EMM2026-51 
Date of Issue 2026-07-06 (ISEC, SITE, BioX, HWS, ICSS, 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 ISEC2026-44 SITE2026-40 BioX2026-67 HWS2026-40 ICSS2026-59 EMM2026-51

Conference Information
Committee BioX HWS ISEC SITE ICSS EMM IPSJ-CSEC IPSJ-SPT 
Conference Date 2026-07-13 - 2026-07-15 
Place (in Japanese) (See Japanese page) 
Place (in English) Sapporo Convention Center 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To EMM 
Conference Code 2026-07-BioX-HWS-ISEC-SITE-ICSS-EMM-CSEC-SPT-SEC 
Language English 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) LoRA-ViT in key-based domain adaptation for learnable image encryption 
Sub Title (in English)  
Keyword(1) privacy-preserving image classification  
Keyword(2) low-rank adaptation  
Keyword(3) image encryption  
Keyword(4) vision transformers  
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1st Author's Name Haiwei Lin  
1st Author's Affiliation Chiba University (Chiba Univ.)
2nd Author's Name Shoko Imaizumi  
2nd Author's Affiliation Chiba University (Chiba Univ.)
3rd Author's Name Hitoshi Kiya  
3rd Author's Affiliation Tokyo Metropolitan University (TMU)
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Speaker Author-1 
Date Time 2026-07-14 15:40:00 
Presentation Time 25 minutes 
Registration for EMM 
Paper # ISEC2026-44, SITE2026-40, BioX2026-67, HWS2026-40, ICSS2026-59, EMM2026-51 
Volume (vol) vol.126 
Number (no) no.107(ISEC), no.108(SITE), no.109(BioX), no.110(HWS), no.111(ICSS), no.112(EMM) 
Page pp.212-217 
#Pages 6 
Date of Issue 2026-07-06 (ISEC, SITE, BioX, HWS, ICSS, EMM) 

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