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Paper Abstract and Keywords
Presentation 2025-12-11 14:30
Segmentation-Based Detection of Inpainted Regions and Exploration of Information Embedding
Haruki Sugimoto, Kenya Jin'no (TCU) NLP2025-63
Abstract (in Japanese) (See Japanese page) 
(in English) This study investigates the detection of inpainted regions in images processed by the learning-based inpainting model LaMa, using a SegNet-based segmentation approach. The model trained with diverse mask shapes accurately detected LaMa-generated regions, whereas its accuracy significantly dropped when applied to images processed by DeepFillv2. Conversely, retraining the model with DeepFillv2 data greatly improved detection accuracy. These results suggest that segmentation models learn texture features specific to each inpainting method, showing limited generalization across different generative models. Moreover, when applying JPEG compression with quality 75, fine texture characteristics unique to LaMa inpainting were lost, making detection based on these features difficult. This indicates that inpainting-derived features are vulnerable to common post-processing operations, while compression naturally causes the detectability of inpainting traces to diminish. In addition, region selection based on Total Variation demonstrated that targeting low-complexity areas can suppress perceptual degradation, providing an effective guideline for region selection in information embedding using generative models.
Keyword (in Japanese) (See Japanese page) 
(in English) inpainting / segmentation / information embedding / / / / /  
Reference Info. IEICE Tech. Rep., vol. 125, no. 283, NLP2025-63, pp. 54-58, Dec. 2025.
Paper # NLP2025-63 
Date of Issue 2025-12-04 (NLP) 
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 NLP2025-63

Conference Information
Committee NLP  
Conference Date 2025-12-11 - 2025-12-12 
Place (in Japanese) (See Japanese page) 
Place (in English) Kochi Castle Museum of History 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Nonlinear problem, etc 
Paper Information
Registration To NLP 
Conference Code 2025-12-NLP 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Segmentation-Based Detection of Inpainted Regions and Exploration of Information Embedding 
Sub Title (in English)  
Keyword(1) inpainting  
Keyword(2) segmentation  
Keyword(3) information embedding  
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1st Author's Name Haruki Sugimoto  
1st Author's Affiliation Tokyo city University (TCU)
2nd Author's Name Kenya Jin'no  
2nd Author's Affiliation Tokyo city University (TCU)
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Speaker Author-1 
Date Time 2025-12-11 14:30:00 
Presentation Time 20 minutes 
Registration for NLP 
Paper # NLP2025-63 
Volume (vol) vol.125 
Number (no) no.283 
Page pp.54-58 
#Pages 5 
Date of Issue 2025-12-04 (NLP) 

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