Open Access

ARTICLE

A Multi-Watermarking Algorithm for Medical Images Using Inception V3 and DCT

Yu Fan^1,6, Jingbing Li^1,2,*, Uzair Aslam Bhatti^1,2, Chunyan Shao¹, Cheng Gong¹, Jieren Cheng^3,5, Yenwei Chen⁴

1 School of Information and Communication Engineering, Hainan University, Haikou, 570100, China
2 State Key Laboratory of Marine Resource Utilization in the South China Sea, Hainan University, Haikou, 570100, China
3 School of Computer Science and Technology, Hainan University, Haikou, 570100, China
4 Graduate School of Information Science and Engineering, Ritsumeikan University, Kyoto, 5258577, Japan
5 Hainan Blockchain Technology Engineering Research Center, Hainan University, Haikou, 570100, China
6 TJ-YZ School of Network Science, Haikou University of Economics, Haikou, 571127, China

* Corresponding Author: Jingbing Li. Email:

Computers, Materials & Continua 2023, 74(1), 1279-1302. https://doi.org/10.32604/cmc.2023.031445

Received 18 April 2022; Accepted 12 June 2022; Issue published 22 September 2022

Abstract

Medical images are a critical component of the diagnostic process for clinicians. Although the quality of medical photographs is essential to the accuracy of a physician’s diagnosis, they must be encrypted due to the characteristics of digital storage and information leakage associated with medical images. Traditional watermark embedding algorithm embeds the watermark information into the medical image, which reduces the quality of the medical image and affects the physicians’ judgment of patient diagnosis. In addition, watermarks in this method have weak robustness under high-intensity geometric attacks when the medical image is attacked and the watermarks are destroyed. This paper proposes a novel watermarking algorithm using the convolutional neural networks (CNN) Inception V3 and the discrete cosine transform (DCT) to address above mentioned problems. First, the medical image is input into the Inception V3 network, which has been structured by adjusting parameters, such as the size of the convolution kernels and the typical architecture of the convolution modules. Second, the coefficients extracted from the fully connected layer of the network are transformed by DCT to obtain the feature vector of the medical image. At last, the watermarks are encrypted using the logistic map system and hash function, and the keys are stored by a third party. The encrypted watermarks and the original image features are performed logical operations to realize the embedding of zero-watermark. In the experimental section, multiple watermarking schemes using three different types of watermarks were implemented to verify the effectiveness of the three proposed algorithms. Our NC values for all the images are more than 90% accurate which shows the robustness of the algorithm. Extensive experimental results demonstrate the robustness under both conventional and high-intensity geometric attacks of the proposed algorithm.

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Keywords

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