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A Dual Model Watermarking Framework for Copyright Protection in Image Processing Networks

Yuhang Meng1, Xianyi Chen1,*, Xingming Sun1, Yu Liu1, Guo Wei2

1 Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing, 210044, China
2 The University of North Carolina, Pembroke, 31321, USA

* Corresponding Author: Xianyi Chen. Email:

Computers, Materials & Continua 2023, 75(1), 831-844.


Image processing networks have gained great success in many fields, and thus the issue of copyright protection for image processing networks has become a focus of attention. Model watermarking techniques are widely used in model copyright protection, but there are two challenges: (1) designing universal trigger sample watermarking for different network models is still a challenge; (2) existing methods of copyright protection based on trigger s watermarking are difficult to resist forgery attacks. In this work, we propose a dual model watermarking framework for copyright protection in image processing networks. The trigger sample watermark is embedded in the training process of the model, which can effectively verify the model copyright. And we design a common method for generating trigger sample watermarks based on generative adversarial networks, adaptively generating trigger sample watermarks according to different models. The spatial watermark is embedded into the model output. When an attacker steals model copyright using a forged trigger sample watermark, which can be correctly extracted to distinguish between the piratical and the protected model. The experiments show that the proposed framework has good performance in different image segmentation networks of UNET, UNET++, and FCN (fully convolutional network), and effectively resists forgery attacks.


Cite This Article

Y. Meng, X. Chen, X. Sun, Y. Liu and G. Wei, "A dual model watermarking framework for copyright protection in image processing networks," Computers, Materials & Continua, vol. 75, no.1, pp. 831–844, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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