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A robust document image watermarking scheme using deep neural network

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Abstract

Watermarking is an important copyright protection technology which generally embeds the identity information into the carrier imperceptibly. Then the identity can be extracted to prove the copyright from the watermarked carrier even after suffering various attacks. Most of the existing watermarking technologies take the nature images as carriers. Different from the natural images, document images are not so rich in color and texture, and thus have less redundant information to carry watermarks. This paper proposes an end-to-end document image watermarking scheme using the deep neural network. Specifically, an encoder and a decoder are designed to embed and extract the watermark. A noise layer is added to simulate the various attacks that could be encountered in reality, such as the Cropout, Dropout, Gaussian blur, Gaussian noise, Resize, and JPEG Compression. A text-sensitive loss function is designed to limit the embedding modification on characters. An embedding strength adjustment strategy is proposed to improve the quality of watermarked image with little loss of extraction accuracy. Experimental results show that the proposed document image watermarking technology outperforms three state-of-the-art methods in terms of the robustness and image quality.

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Some or all data, models, or code generated or used during the study are available in the submitted article.

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Acknowledgements

This work is supported in part by the National Key Research and Development Plan of China under Grant number 2022YFB3103100, 2020YFB1005600, in part by Guangdong Basic and Applied Basic Research Foundation under Grant number 2019B1515120010, in part by the National Natural Science Foundation of China under grant numbers 62122032, 62172233, 62102189, U1936118, 61932011, 61931004, 61825203, U1736203, and 61732021, in part by the Major Program of Guangdong Basic and Applied Research Project under Grant 2019B030302008, in part by Six Peak Talent project of Jiangsu Province (R2016L13), Qinglan Project of Jiangsu Province, and “333” project of Jiangsu Province, in part by the National Joint Engineering Research Center for Network Security Detection and Protection Technology, in part by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD) fund, in part by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) fund, China. Zhihua Xia is supported by BK21+ program from the Ministry of Education of Korea.

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Authors and Affiliations

  1. School of Computer, Nanjing University of Information Science & Technology, 219 Ningliu Road, Nanjing, 210044, Jiangsu, China

    Sulong Ge, Zhihua Xia & Jianwei Fei

  2. Engineering Research Center of Digital Forensics, Ministry of Education, 219 Ningliu Road, Nanjing, 210044, Jiangsu, China

    Sulong Ge, Zhihua Xia & Jianwei Fei

  3. College of Cyber Security, Jinan University, 601 Huangpu Road West, Guangzhou, 510632, Guangdong, China

    Zhihua Xia, Jian Weng & Ming Li

  4. Guangzhou Fongwell Data Limited Company, 371-1 Wushan Road, Guangzhou, 510510, Guangdong, China

    Yao Tong

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  1. Sulong Ge
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Correspondence to Zhihua Xia.

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Ge, S., Xia, Z., Fei, J. et al. A robust document image watermarking scheme using deep neural network. Multimed Tools Appl 82, 38589–38612 (2023). https://doi.org/10.1007/s11042-023-15048-y

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  • DOI: https://doi.org/10.1007/s11042-023-15048-y

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