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Robust Multi-Watermarking Algorithm for Medical Images Based on GoogLeNet and Henon Map

Wenxing Zhang1, Jingbing Li1,2,*, Uzair Aslam Bhatti1,2, Jing Liu3, Junhua Zheng1, Yen-Wei Chen4

1 School of Information and Communication Engineering, Hainan University, Haikou, 570228, China
2 State Key Laboratory of Marine Resource Utilization in the South China Sea, Hainan University, Haikou, 570228, China
3 Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, 311100, China
4 Graduate School of Information Science and Engineering, Ritsumeikan University, Kusatsu, 525-8577, Japan

* Corresponding Author: Jingbing Li. Email: email

Computers, Materials & Continua 2023, 75(1), 565-586. https://doi.org/10.32604/cmc.2023.036317

Abstract

The field of medical images has been rapidly evolving since the advent of the digital medical information era. However, medical data is susceptible to leaks and hacks during transmission. This paper proposed a robust multi-watermarking algorithm for medical images based on GoogLeNet transfer learning to protect the privacy of patient data during transmission and storage, as well as to increase the resistance to geometric attacks and the capacity of embedded watermarks of watermarking algorithms. First, a pre-trained GoogLeNet network is used in this paper, based on which the parameters of several previous layers of the network are fixed and the network is fine-tuned for the constructed medical dataset, so that the pre-trained network can further learn the deep convolutional features in the medical dataset, and then the trained network is used to extract the stable feature vectors of medical images. Then, a two-dimensional Henon chaos encryption technique, which is more sensitive to initial values, is used to encrypt multiple different types of watermarked private information. Finally, the feature vector of the image is logically operated with the encrypted multiple watermark information, and the obtained key is stored in a third party, thus achieving zero watermark embedding and blind extraction. The experimental results confirm the robustness of the algorithm from the perspective of multiple types of watermarks, while also demonstrating the successful embedding of multiple watermarks for medical images, and show that the algorithm is more resistant to geometric attacks than some conventional watermarking algorithms.

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APA Style
Zhang, W., Li, J., Bhatti, U.A., Liu, J., Zheng, J. et al. (2023). Robust multi-watermarking algorithm for medical images based on googlenet and henon map. Computers, Materials & Continua, 75(1), 565-586. https://doi.org/10.32604/cmc.2023.036317
Vancouver Style
Zhang W, Li J, Bhatti UA, Liu J, Zheng J, Chen Y. Robust multi-watermarking algorithm for medical images based on googlenet and henon map. Comput Mater Contin. 2023;75(1):565-586 https://doi.org/10.32604/cmc.2023.036317
IEEE Style
W. Zhang, J. Li, U.A. Bhatti, J. Liu, J. Zheng, and Y. Chen "Robust Multi-Watermarking Algorithm for Medical Images Based on GoogLeNet and Henon Map," Comput. Mater. Contin., vol. 75, no. 1, pp. 565-586. 2023. https://doi.org/10.32604/cmc.2023.036317



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