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Robust Zero-Watermarking of Color Medical Images Using Multi-Channel Gaussian-Hermite Moments and 1D Chebyshev Chaotic Map

Research Authors
Doaa Sami Khafaga , Amel Ali Alhussan , Mohamed M. Darwish and Khalid M. Hosny
Research Date
Research Department
Research Journal
Sensors
Research Publisher
MDPI
Research Vol
22(15)
Research Website
https://www.mdpi.com/1424-8220/22/15/5612
Research Year
2022
Research_Pages
5612
Research Abstract

Copyright protection of medical images is a vital goal in the era of smart healthcare
systems. In recent telemedicine applications, medical images are sensed using medical imaging
devices and transmitted to remote places for screening by physicians and specialists. During their
transmission, the medical images could be tampered with by intruders. Traditional watermarking
methods embed the information in the host images to protect the copyright of medical images.
The embedding destroys the original image and cannot be applied efficiently to images used in
medicine that require high integrity. Robust zero-watermarking methods are preferable over other
watermarking algorithms in medical image security due to their outstanding performance. Most
existing methods are presented based on moments and moment invariants, which have become a
prominent method for zero-watermarking due to their favorable image description capabilities and
geometric invariance. Although moment-based zero-watermarking can be an effective approach to
image copyright protection, several present approaches cannot effectively resist geometric attacks,
and others have a low resistance to large-scale attacks. Besides these issues, most of these algorithms
rely on traditional moment computation, which suffers from numerical error accumulation, leading to
numerical instabilities, and time consumption and affecting the performance of these moment-based
zero-watermarking techniques. In this paper, we derived multi-channel Gaussian–Hermite moments
of fractional-order (MFrGHMs) to solve the problems. Then we used a kernel-based method for the
highly accurate computation of MFrGHMs to solve the computation issue. Then, we constructed
image features that are accurate and robust. Finally, we presented a new zero-watermarking scheme
for color medical images using accurate MFrGHMs and 1D Chebyshev chaotic features to achieve
lossless copyright protection of the color medical images. We performed experiments where their
outcomes ensure the robustness of the proposed zero-watermarking algorithms against various
attacks. The proposed zero-watermarking algorithm achieves a good balance between robustness and
imperceptibility. Compared with similar existing algorithms, the proposed algorithm has superior
robustness, security, and time computation.