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Paper Abstract and Keywords
Presentation 2026-07-14 16:05
Enhanced Robustness against Deep Model Inversion Attacks Using Learnable Image Encryption
Mare Hirose, Shoko Imaizumi (Chiba Univ.), Hitoshi Kiya (TMU) ISEC2026-45 SITE2026-41 BioX2026-68 HWS2026-41 ICSS2026-60 EMM2026-52
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we propose a method that applies learnable image encryption, which can be directly used for model training, to training images in order to improve robustness against deep model inversion attacks. Deep model inversion attacks are serious threats to the privacy of training images, as they exploit model outputs or internal information together with generative models to reconstruct training images with high accuracy. In the proposed method, a model is trained using encrypted training images, thereby maintaining classification performance while improving robustness against reconstruction attacks. In the experiments, using a face image dataset as an example, we apply deep model inversion attacks under multiple encryption conditions and evaluate the effectiveness of the proposed method both qualitatively and quantitatively based on the visual characteristics of the reconstructed images and the classification results obtained by a face recognition model.
Keyword (in Japanese) (See Japanese page) 
(in English) Learnable image encryption / model inversion attack / privacy preserving / / / / /  
Reference Info. IEICE Tech. Rep., vol. 126, no. 112, EMM2026-52, pp. 218-223, July 2026.
Paper # EMM2026-52 
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 ISEC2026-45 SITE2026-41 BioX2026-68 HWS2026-41 ICSS2026-60 EMM2026-52

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 Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Enhanced Robustness against Deep Model Inversion Attacks Using Learnable Image Encryption 
Sub Title (in English)  
Keyword(1) Learnable image encryption  
Keyword(2) model inversion attack  
Keyword(3) privacy preserving  
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1st Author's Name Mare Hirose  
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 16:05:00 
Presentation Time 25 minutes 
Registration for EMM 
Paper # ISEC2026-45, SITE2026-41, BioX2026-68, HWS2026-41, ICSS2026-60, EMM2026-52 
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.218-223 
#Pages 6 
Date of Issue 2026-07-06 (ISEC, SITE, BioX, HWS, ICSS, EMM) 

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