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A convolutional neural network-based blind robust image watermarking approach exploiting the frequency domain

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Abstract

Image watermarking embeds information in the image that is visually imperceptible and can be recovered even if the image is modified or attacked during distribution, thus protecting the image copyright. Current image watermarking methods make the learned model resistant to attacks by simulating specific attacks but lack robustness to unspecified attacks. In this paper, we propose to hide the information in the frequency domain. To control the distribution and intensity of watermarking information, we introduce a channel weighting module based on modified Gaussian distribution. In the spatial domain, we design a spatial weighting module to improve the watermarking visual quality. Moreover, a channel attention enhancement module designed in the frequency domain senses the distribution of watermarking information and enhances the frequency domain channel signals to improve the watermarking robustness. Abundant experimental results show that our method guarantees high image visual quality and high watermarking capacity. The generated watermarking images can robustly resist unspecified attacks such as noise, crop, blur, color transform, JPEG compression, and screen-shooting.

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Data availability

The datasets generated during the current study are available from the corresponding author on reasonable request.

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Funding

This work was supported in part by the Fundamental Research Funds for the Central Universities (No. 2021ZY86), the Natural Science Foundation of China (NSFC) (No. 61703046) and the open fund of Science and Technology on Complex Electronic System Simulation Laboratory (No. 614201004012102).

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Authors and Affiliations

  1. School of Information Science and Technology, Beijing Forestry University, Beijing, China

    Zhiwei Zhang, Han Wang & Hui Fu

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  1. Zhiwei Zhang
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  2. Han Wang
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  3. Hui Fu
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Correspondence to Han Wang.

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Zhang, Z., Wang, H. & Fu, H. A convolutional neural network-based blind robust image watermarking approach exploiting the frequency domain. Vis Comput 39, 3533–3544 (2023). https://doi.org/10.1007/s00371-023-02967-y

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