Robust Watermarking Algorithm for Medical Volume Data in Internet of Medical Things

Abstract

The advancement of 5G technology, big data and cloud storage has promoted the rapid development of the Internet of Medical Things (IoMT). Based on the strict security requirements and high level of accuracy required for disease diagnosis and pathological analysis, 3D medical volume data have been created in large numbers. The IoMT facilitates the rapid transfer of medical information and also makes the protection of pathological information and privacy information of patients increasingly prominent. To solve the security problem, a robust zero-watermarking algorithm based on 3D hyperchaos and 3D dualtree complex wavelet transform is proposed according to the selected feature of medical volume data. The feature combines human visual features with improved perceptual hashing techniques. It is a robust and efficient binary sequence. When implementing the proposed algorithm, the watermark is first scrambled with 3D hyperchaos to enhance security. Then, 3D DTCWT-DCT transformation is applied to medical volume data, and the low-frequency coefficients that can represent the features are selected and binarized to generate the secret key to complete the watermark embedding and extraction. Zero embedding and blind extraction ensure that the original medical volume data is not altered in any form, which meets the special requirements for diagnosis. Simulation results show that the algorithm is robust and can effectively resist common attacks and geometric attacks. It used fewer robust features to effectively bound medical volume data and watermark information, saved bandwidth, and satisfied the security of transmission and storage of medical volume data in the Internet of medical things. In particular, compared with state-of-the-art technology, the proposed algorithm improves the average NC value by 46.67% under geometric attacks. INDEX TERMS Hyperchaos, Internet of medical things, medical volume data, robust watermark, 3D-DTCWT-DCT.

A Novel FDCT-SVD Based Watermarking with Radon Transform for Telemedicine Applications

“…The are several watermarking methods proposed in the literature for example, JING LIU et al [7] introduced a zero-watermarking scheme based on 3D dual-tree complex wavelet and 3D hyper-chaos, the scheme proposed based on the features selected from medical volume data. The scheme enhanced the NC value average by 46.67 % against geometric attacks and fulfilled transmission and storage security of medical data but lacks imperceptibility recommended for medical images.…”

A novel zero-watermarking scheme with enhanced distinguishability and robustness for volumetric medical imaging

“…Han et al [31,32] design a robust zerowatermarking system for medical volume data which combines three-dimensional discrete cosine transform (3D-DCT) and the chaotic neural network. They fur-ther extend these 3D-DCT based methods into three-dimensional dual tree complex wavelet transform (3D-DTCWT) domain [33] to enhance the watermarking robustness. Fan et al [34] propose a medical image watermarking algorithm based on the significant difference of cellular automata transform (CAT) for copyright protection to achieve sufficient security and robustness.…”

“…Three categories of digital watermarking schemes are suitable for authenticity and copyright protection of medical images, which are ROI lossless watermarking [10][11][12][13][14][15][16][17][18]; reversible watermarking [19][20][21][22][23][24][25][26][27][28][29]; and zero-watermarking [30][31][32][33][34][35][36][37][38][39][40]. ROI lossless watermarking schemes embed watermark into medical images while keeping their ROI regions distortion-free to minimize the bias on medical diagnosis.…”

Reliable Internet of Things: Challenges and Future Trends

“…Internet of Medical Things (IoMT) is a vital enabler for the future health industry. IoMT can provide applications such as remote patient monitoring, automatic monitoring of emergency patients, medical supply chain control, and contact tracing in pandemic situations [48][49][50][51][52][53][54][55][56].…”