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Paper Abstract and Keywords
Presentation 2026-03-05 15:00
[Poster Presentation] Single-Layered Variable-Length Encoding Networks for DiffuseTrace and Its Performance Evaluation
Ou Egami, Masaki Kawamura (Yamaguchi Univ.) EMM2025-124
Abstract (in Japanese) (See Japanese page) 
(in English) We address the issue of message length constraints in DiffuseTrace, a watermarking technique based on latent diffusion models. We propose a new encoding method utilizing lightweight random networks. While the original DiffuseTrace required reconstructing the VAE whenever the message length was changed, our proposed method introduces random networks for message encoding and decoding, enabling the handling of variable-length messages without VAE reconstructing. In this study, we simplify the random network structure from two layers to one and examine the impact of this lightweight design on performance. Experimental results demonstrate that the single-layer model achieves higher extraction accuracy (Acc = 0.993) than the previous two-layer model for message lengths $N leq 1024$. This shows that network simplification can improve accuracy. Furthermore, evaluations using NIQE, PIQE, and CLIP scores confirm that the generated images maintain high perceptual quality and semantic consistency with the prompt even after watermark embedding. The method also exhibits robust resistance against various image attacks. These results indicate that the proposed lightweight model achieves high-precision, flexible watermarking at a low computational cost.
Keyword (in Japanese) (See Japanese page) 
(in English) DiffuseTrace / Image Generative AI / Latent Diffusion Model / Digital Watermarking / Random Network / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 396, EMM2025-124, pp. 41-46, March 2026.
Paper # EMM2025-124 
Date of Issue 2026-02-26 (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 EMM2025-124

Conference Information
Committee EMM  
Conference Date 2026-03-05 - 2026-03-06 
Place (in Japanese) (See Japanese page) 
Place (in English) Miyako Island Hirara Port Terminal Bldg. 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To EMM 
Conference Code 2026-03-EMM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Single-Layered Variable-Length Encoding Networks for DiffuseTrace and Its Performance Evaluation 
Sub Title (in English)  
Keyword(1) DiffuseTrace  
Keyword(2) Image Generative AI  
Keyword(3) Latent Diffusion Model  
Keyword(4) Digital Watermarking  
Keyword(5) Random Network  
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1st Author's Name Ou Egami  
1st Author's Affiliation Yamaguchi University (Yamaguchi Univ.)
2nd Author's Name Masaki Kawamura  
2nd Author's Affiliation Yamaguchi University (Yamaguchi Univ.)
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Speaker Author-1 
Date Time 2026-03-05 15:00:00 
Presentation Time 60 minutes 
Registration for EMM 
Paper # EMM2025-124 
Volume (vol) vol.125 
Number (no) no.396 
Page pp.41-46 
#Pages 6 
Date of Issue 2026-02-26 (EMM) 

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