Copyright protection of multiple CT images using Octonion Krawtchouk moments and grey Wolf optimizer |
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| Institution: | 1. Laboratory of Electronic Signals and Systems of Information, Faculty of Science, Sidi Mohamed Ben Abdellah-Fez University, Fez, Morocco;2. Laboratory of Engineering, Systems and Applications, National School of Applied Sciences, Sidi Mohamed Ben Abdellah-Fez University, Fez, Morocco;3. Faculty of Medicine and Pharmacy, Clinical Neuroscience Laboratory, Sidi Mohamed Ben Abdellah University, Fez, Morocco;4. Department of Radiology and Clinical Imaging, University Hospital of Fez, Fez, Morocco;5. Faculty of Medicine and Pharmacy, Department of Biophysics and Clinical MRI Methods, Sidi Mohamed Ben Abdellah University, BP. 893, Km 2.200, Sidi Hrazem Road, 30000 Fez, Morocco;6. Systems and Sustainable Environment Laboratory (SED), Faculty of Engineering Sciences (FSI) Private University of Fez (UPF), Fez, Morocco;1. School of Automation, Nanjing University of Science and Technology, Nanjing, China;2. IBISC, Evry Val-dEssonne University, Universite Paris-Saclay, Evry, France;3. Industrial Center/School of Innovation and Entrepreneurship, Nanjing Institute of Technology, Nanjing, China;1. Artificial Intelligence Energy Research Institute, Northeast Petroleum University, Daqing, 163318, China;2. Heilongjiang Provincial Key Laboratory of Networking and Intelligent Control, Northeast Petroleum University, Daqing, 163318, China;3. Sanya Offshore Oil & Gas Research Institute, Northeast Petroleum University, Sanya 572025, China;4. School of Petroleum Engineering, Northeast Petroleum University, Daqing, 163318, China;5. Key Laboratory of Enhanced Oil and Gas Recovery (Northeast Petroleum University), Ministry of Education, Daqing, 163318, China;6. School of Physics and Electronic Engineering, Northeast Petroleum University, Daqing, 163318, China;1. School of Science, Harbin Institute of Technology, Shenzhen, China;2. School of Automation Science and Engineering, South China University of Technology, Guangzhou, China;3. Pazhou Laboratory, Guangzhou, 510330, China;4. Department of Applied Mathematics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong;5. The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, 518057, China |
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| Abstract: | This paper proposes a novel Octonion Krawtchouk Moments (OKMs) transform to deal with a set of images in a compact manner, and based on this transform, a local zero-watermarking scheme is proposed to protect the copyright of CT medical images. The scheme first annotates regions of interest (ROIs) on seven medical images and then uses the OKMs of these ROIs to construct a single feature image called zero-watermark. This scheme adopts the gray Wolf Optimizer (GWO) algorithm to have a blind nature and to improve robustness against common image processing manipulations and attacks (zero-watermarking requirements). In addition, our scheme uses the trained U-net (R231) model to reduce the search space for the GWO algorithm and prevent this algorithm from getting stuck in a local optimal solution. The experimental results show that the proposed method is very robust against common image processing manupilations and attacks and has superiority compared with superb other zero-watermarking methods. |
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