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International Workshop on Digital Watermarking

IWDW 2022: Digital Forensics and Watermarking pp 162–173Cite as

Adaptive Robust Watermarking Method Based on Deep Neural Networks

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Part of the Lecture Notes in Computer Science book series (LNCS,volume 13825)

Abstract

Aiming at the problem of digital multimedia piracy and infringement, an adaptive robust watermarking algorithm based on Deep Neural Networks (DNNs) is proposed. In our method, the watermark sequence to be embedded is mapped to a noise pattern first, which has the same dimension as the carrier image. Specifically, the noise pattern is generated adaptively according to the statistical properties of the carrier image, in which the noise intensity corresponding to the texture area of the carrier image is large, and that corresponding to the smooth area is small. Thus, after adding the generated noise pattern to the carrier image, good visual quality can be easily obtained. Furthermore, considering a series of attacks such as adding noise and JPEG compression, the watermark encoder and decoder in our scheme are jointly trained to resist the potential attacks in the physical world. Experimental results demonstrate that better visual quality and higher robustness can be obtained compared with those state-of-the-art algorithms based on DNNs. This means that we have better solved the problem of mutual restriction between visual quality and robustness.

Keywords

  • Robust watermarking
  • Adaptive strategy
  • Deep neural networks

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  • DOI: 10.1007/978-3-031-25115-3_11
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Acknowledgments

This work was supported by the National Natural Science Foun-dation of China (62072481, 61772572), and the Science and Technology Program of Guangzhou, China (202201011587).

Author information

Authors and Affiliations

  1. School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, 510006, China

    Fan Li & Chen Wan

  2. School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen, 518107, China

    Fangjun Huang

  3. Guangdong Provincial Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou, 510006, China

    Fan Li, Chen Wan & Fangjun Huang

Authors
  1. Fan Li
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  2. Chen Wan
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  3. Fangjun Huang
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Corresponding author

Correspondence to Fangjun Huang .

Editor information

Editors and Affiliations

  1. Chinese Academy of Sciences, Institute of Information Engineering, Beijing, China

    Xianfeng Zhao

  2. Guangxi Normal University, Guilin, China

    Zhenjun Tang

  3. Universidade de Vigo, Vigo, Spain

    Pedro Comesaña-Alfaro

  4. University of Florence, Florence, Italy

    Alessandro Piva

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Li, F., Wan, C., Huang, F. (2023). Adaptive Robust Watermarking Method Based on Deep Neural Networks. In: Zhao, X., Tang, Z., Comesaña-Alfaro, P., Piva, A. (eds) Digital Forensics and Watermarking. IWDW 2022. Lecture Notes in Computer Science, vol 13825. Springer, Cham. https://doi.org/10.1007/978-3-031-25115-3_11

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  • DOI: https://doi.org/10.1007/978-3-031-25115-3_11

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