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Model Watermarking for Deep Neural Networks of Image Recovery

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Digital Watermarking for Machine Learning Model

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Abstract

Recently, there is an increasing interest in applying deep learning to image recovery, an important problem in low-level vision. Publishing pre-trained DNN models of image recovery has become popular in the society. As a result, how to protect the intellectual property of the owners of those models has been a serious concern. To address it, this chapter introduces a framework developed in our recent work for watermarking the DNN models of image recovery. The DNNs of image recovery differ much from those of image classification in various aspects. Such differences pose additional challenges to the model watermarking, but meanwhile they also bring chances for improvement. Using image denoising and image super-resolution as case studies, we present a black-box watermarking approach for pre-trained models, which exploits the over-parameterization property of an image recovery DNN. Moreover, a watermark visualization method is introduced for additional subjective verification.

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Authors and Affiliations

  1. South China University of Technology, Guangzhou, China

    Yuhui Quan & Huan Teng

Authors
  1. Yuhui Quan
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  2. Huan Teng
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Correspondence to Yuhui Quan .

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Editors and Affiliations

  1. AI Lab, WeBank, Shenzhen, China

    Lixin Fan

  2. Department of Artificial Intelligence, Universiti Malaya, Kuala Lumpur, Malaysia

    Chee Seng Chan

  3. Department of CS and Engineering, Hong Kong University of Science and Tech, Hong Kong, China

    Qiang Yang

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Quan, Y., Teng, H. (2023). Model Watermarking for Deep Neural Networks of Image Recovery. In: Fan, L., Chan, C.S., Yang, Q. (eds) Digital Watermarking for Machine Learning Model. Springer, Singapore. https://doi.org/10.1007/978-981-19-7554-7_3

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  • DOI: https://doi.org/10.1007/978-981-19-7554-7_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-19-7553-0

  • Online ISBN: 978-981-19-7554-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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