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Paper Abstract and Keywords
Presentation 2022-03-08 09:55
[Poster Presentation] Study on JPEG Compression Resistant Watermarking Method Trained with Quantized Activation Function
Shingo Yamauchi, Masaki Kawamura (Yamaguchi Univ.) EMM2021-110
Abstract (in Japanese) (See Japanese page) 
(in English) We propose a watermarking method that introduces a quantized activation function to acquire robustness against quantization for JPEG compression. Neural network-based methods have been proposed as watermarking methods that are robust against various types of attacks. Zhu et al. proposed a watermarking method that is robust against JPEG compression, clipping and Gaussian blurring. They showed that it was possible to increase the robustness by including an additional layer that simulated these attacks between the generation layer of the stego image and the extraction layer of the watermarks. However, the method of Zhu et al. did not provide sufficient robustness against JPEG compression compared to other attacks. This was because their method did not implement the quantization function in JPEG compression. Therefore, we propose a quantized activation function, which consists of shifted hyperbolic tangent functions. In this study, we introduce the quantized activation function to the watermarking method proposed by Hamamoto and Kawamura, and evaluate the effect of quantization. Their method has no attack layer, by introducing an attack layer with a quantized activation function, we can evaluate the JPEG compression robustness. The bit error rate (BER) was used to evaluate the robustness, and the PSNR was used for image quality. As a result, although the image quality decreased by introducing the quantized activation function, the watermarks could be extracted with the BER of less than 0.1 at the Q-value of 20 or higher in JPEG compression. Therefore, by introducing the quantized activation function, the compression robustness was achieved.
Keyword (in Japanese) (See Japanese page) 
(in English) digital watermarking / autoencoder / JPEG compression / activation function / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 417, EMM2021-110, pp. 95-100, March 2022.
Paper # EMM2021-110 
Date of Issue 2022-02-28 (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 EMM2021-110

Conference Information
Committee EMM  
Conference Date 2022-03-07 - 2022-03-08 
Place (in Japanese) (See Japanese page) 
Place (in English) (Primary: Online, Secondary: On-site) 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Image and Sound Quality, Metrics for Perception and Recognition, Human Auditory and Visual System, etc. 
Paper Information
Registration To EMM 
Conference Code 2022-03-EMM 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Study on JPEG Compression Resistant Watermarking Method Trained with Quantized Activation Function 
Sub Title (in English)  
Keyword(1) digital watermarking  
Keyword(2) autoencoder  
Keyword(3) JPEG compression  
Keyword(4) activation function  
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1st Author's Name Shingo Yamauchi  
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 2022-03-08 09:55:00 
Presentation Time 15 minutes 
Registration for EMM 
Paper # EMM2021-110 
Volume (vol) vol.121 
Number (no) no.417 
Page pp.95-100 
#Pages 6 
Date of Issue 2022-02-28 (EMM) 

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