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

作　　者：Wenxing Zhang Jingbing Li Uzair Aslam Bhatti Jing Liu Junhua Zheng Yen-Wei Chen 

机构地区：[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

出　　处：《Computers, Materials & Continua》2023年第4期565-586,共22页计算机、材料和连续体（英文）

基　　金：supported in part by the Natural Science Foundation of China under Grants 62063004;the Key Research Project of Hainan Province under Grant ZDYF2021SHF Z093;the Hainan Provincial Natural Science Foundation of China under Grants 2019RC018 and 619QN246;the postdoctor research from Zhejiang Province under Grant ZJ2021028.

摘　　要：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 confirmthe robustness of the algorithm from the perspective ofmultiple types of watermarks,while also demonstrating the successful embedding ofmultiple watermarks for medical images,and show that the algorithm is more resistant to geometric attacks than some conventional watermarking algorithms.

关 键 词：Zero watermarks GoogLeNet medical image Henon map feature vector 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]