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IrisMarkNet: Iris feature watermarking embedding and extraction network for image copyright protection

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Abstract

Based on the urgent need for copyright protection in the digital era, the accuracy of iris recognition technology for authentication must be considered. Ensuring the accuracy of high-precision authentication has tremendous challenges in improving imperceptibility and robustness, especially the longer iris features, as watermarks lead to reduced imperceptibility. A novel digital watermarking method called IrisMarkNet, which embeds the copyright owner’s binary iris features into the cover image based on deep neural networks, is first proposed to protect image copyright. It utilizes a novel pyramid feature fusion module PFF based on a multiscale feature fusion strategy to obtain better imperceptibility and well-enhanced robustness, which performs better than other watermark algorithms adopting single-scale feature fusion. Additionally, for different mini-batches, the noise in the noise layer is randomly selected for adversarial training to advance the robustness of the proposed model. In addition, we suggest utilizing Convolutional Block Attention Module (CBAM) Woo et al (1), which can help to learn better iris features in the decoding stage and propose a novel authenticator to achieve authentication of the image copyright owner. The extensive experimental and comparative results have demonstrated the superior performance of the proposed scheme compared with the state-of-the-art watermark algorithms. Under all experimental distortions, such as JPEG compression, crop attack, Gaussian filter, salt-and-pepper noise, Gaussian noise, and median filter, IrisMarkNet realizes well-improved robustness and imperceptibility along with a good accuracy rate in the authentication of digital images.

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Authors and Affiliations

  1. Department of Electronic and Information Engineering, Shanghai University of Electric Power, Shanghai, 200120, China

    Wenzhong Shen, Ji Rong, Yingfeng Liu & Yan Zhao

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  1. Wenzhong Shen
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  2. Ji Rong
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  3. Yingfeng Liu
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  4. Yan Zhao
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Correspondence to Ji Rong.

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Shen, W., Rong, J., Liu, Y. et al. IrisMarkNet: Iris feature watermarking embedding and extraction network for image copyright protection. Appl Intell 53, 9992–10008 (2023). https://doi.org/10.1007/s10489-022-04047-5

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