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Taj R, Tao F, Kanwal S, Almogren A, Altameem A, Ur Rehman A. A reversible-zero watermarking scheme for medical images. Sci Rep 2024; 14:17320. [PMID: 39068181 PMCID: PMC11283505 DOI: 10.1038/s41598-024-67672-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 07/15/2024] [Indexed: 07/30/2024] Open
Abstract
The paper addresses the issue of ensuring the authenticity and copyright of medical images in telemedicine applications, with a specific emphasis on watermarking methods. While several systems only concentrate on identifying tampering in medical images, others also provide the capacity to restore the tampered regions upon detection. While several authentication techniques in medical imaging have successfully achieved their goals, previous research underscores a notable deficiency: the resilience of these schemes against unintentional attacks has not been sufficiently examined or emphasized in previous research. This indicates the need for further development and investigation in improving the robustness of medical image authentication techniques against unintentional attacks. This research proposes a Reversible-Zero Watermarking approach as a solution to address these problems. The new approach merges the advantages of both the reversible and zero watermarking techniques. This system is comprised of two parts. The first part is a zero-watermarking technique that uses VGG19-based feature extraction and watermark information to establish an ownership share. The second part incorporates this ownership share into the image in a reversible manner using a combination of a discrete wavelet transform, an integer wavelet transform, and a difference expansion. Research findings confirm that the suggested watermarking approach for medical images demonstrates substantial enhancements compared to current methodologies. Research findings indicate that NC values are often around 0.9 for different attacks, whereas BER values are close to 0. It demonstrates exceptional qualities in being imperceptible, distinguishable, and robust. Additionally, the system provides a persistent verification feature that functions independently of disputes or third-party storage, making it the preferred choice in the domain of medical image watermarking.
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Affiliation(s)
- Rizwan Taj
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, Gansu, China
| | - Feng Tao
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, Gansu, China.
| | - Saima Kanwal
- School of Computer and Communication, Lanzhou University of Technology, Lanzhou, Gansu, China
| | - Ahmad Almogren
- Department of Computer Science, College of Computer and Information Sciences, King Saud University, 11633, Riyadh, Saudi Arabia
| | - Ayman Altameem
- Department of Natural and Engineering Sciences, College of Applied Studies and Community Services, King Saud University, 11543, Riyadh, Saudi Arabia
| | - Ateeq Ur Rehman
- School of Computing, Gachon University, Seongnam, 13120, Republic of Korea
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Alarood AA, Faheem M, Al‐Khasawneh MA, Alzahrani AIA, Alshdadi AA. Secure medical image transmission using deep neural network in e-health applications. Healthc Technol Lett 2023; 10:87-98. [PMID: 37529409 PMCID: PMC10388229 DOI: 10.1049/htl2.12049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 06/13/2023] [Accepted: 07/03/2023] [Indexed: 08/03/2023] Open
Abstract
Recently, medical technologies have developed, and the diagnosis of diseases through medical images has become very important. Medical images often pass through the branches of the network from one end to the other. Hence, high-level security is required. Problems arise due to unauthorized use of data in the image. One of the methods used to secure data in the image is encryption, which is one of the most effective techniques in this field. Confusion and diffusion are the two main steps addressed here. The contribution here is the adaptation of the deep neural network by the weight that has the highest impact on the output, whether it is an intermediate output or a semi-final output in additional to a chaotic system that is not detectable using deep neural network algorithm. The colour and grayscale images were used in the proposed method by dividing the images according to the Region of Interest by the deep neural network algorithm. The algorithm was then used to generate random numbers to randomly create a chaotic system based on the replacement of columns and rows, and randomly distribute the pixels on the designated area. The proposed algorithm evaluated in several ways, and compared with the existing methods to prove the worth of the proposed method.
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Affiliation(s)
| | - Muhammad Faheem
- School of Technology and InnovationsUniversity of VaasaVaasaFinland
| | - Mahmoud Ahmad Al‐Khasawneh
- School of Information TechnologySkyline University CollegeUniversity City SharjahSharjahUnited Arab Emirates
| | - Abdullah I. A. Alzahrani
- Department of Computer Science, Collage of Science and Humanities in Al QuwaiiyahShaqra UniversityShaqraSaudi Arabia
| | - Abdulrahman A. Alshdadi
- Department of Information Systems and Technology, College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia
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Gull S, Parah SA. Advances in medical image watermarking: a state of the art review. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-41. [PMID: 37362709 PMCID: PMC10161187 DOI: 10.1007/s11042-023-15396-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/07/2022] [Revised: 02/21/2023] [Accepted: 04/18/2023] [Indexed: 06/28/2023]
Abstract
Watermarking has been considered to be a potent and persuasive gizmo for its application in healthcare setups that work online, especially in the current COVID-19 scenario. The security and protection of medical image data from various manipulations that take place over the internet is a topic of concern that needs to be addressed. A detailed review of security and privacy protection using watermarking has been presented in this paper. Watermarking of medical images helps in the protection of image content, authentication of Electronic Patient Record (EPR), and integrity verification. At first, we discuss the various prerequisites of medical image watermarking systems, followed by the classification of Medical Image Watermarking Techniques (MIWT) that include state-of-the-art. We have classified MIWT's into four broader classes for providing better understanding of medical image watermarking. The existing schemes have been presented along with their cons so that the reader may be able to grasp the shortcomings of the technique in order to develop novel techniques proving the inevitability of the presented review. Further, various evaluation parameters along with potential challenges pertaining to medical image watermarking systems have been discussed to provide a deep insight into this research area.
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Affiliation(s)
- Solihah Gull
- Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, 190006 India
| | - Shabir A. Parah
- Department of Electronics and Instrumentation Technology, University of Kashmir, Srinagar, 190006 India
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Sinhal R, Ansari IA. Machine learning based multipurpose medical image watermarking. Neural Comput Appl 2023:1-22. [PMID: 37362569 PMCID: PMC10036986 DOI: 10.1007/s00521-023-08457-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 03/03/2023] [Indexed: 03/26/2023]
Abstract
Digital data security has become an exigent area of research due to a huge amount of data availability at present time. Some of the fields like medical imaging and medical data sharing over communication platforms require high security against counterfeit access, manipulation and other processing operations. It is essential because the changed/manipulated data may lead to erroneous judgment by medical experts and can negatively influence the human's heath. This work offers a blind and robust medical image watermarking framework using deep neural network to provide effective security solutions for medical images. During watermarking, the region of interest (ROI) data of the original image is preserved by employing the LZW (Lampel-Ziv-Welch) compression algorithm. Subsequently the robust watermark is inserted into the original image using IWT (integer wavelet transform) based embedding approach. Next, the SHA-256 algorithm-based hash keys are generated for ROI and RONI (region of non-interest) regions. The fragile watermark is then prepared by ROI recovery data and the hash keys. Further, the LSB replacement-based insertion mechanism is utilized to embed the fragile watermark into RONI embedding region of robust watermarked image. A deep neural network-based framework is used to perform robust watermark extraction for efficient results with less computational time. Simulation results verify that the scheme has significant imperceptibility, efficient robust watermark extraction, correct authentication and completely reversible nature for ROI recovery. The relative investigation with existing schemes confirms the dominance of the proposed work over already existing work.
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Affiliation(s)
- Rishi Sinhal
- Electronics and Communication Engineering, PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur, Madhya Pradesh 482005 India
| | - Irshad Ahmad Ansari
- Electronics and Communication Engineering, PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur, Madhya Pradesh 482005 India
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Golea NEH, Melkemi KE, Behloul A. Zero-bit fragile watermarking for medical image tamper detection and recovery using RS code and lifting wavelet transform. THE IMAGING SCIENCE JOURNAL 2023. [DOI: 10.1080/13682199.2022.2161144] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
| | | | - Ali Behloul
- Department of Computer Science, University of Batna 2, Batna, Algeria
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Sinhal R, Sharma S, Ansari IA, Bajaj V. Multipurpose medical image watermarking for effective security solutions. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:14045-14063. [PMID: 35233177 PMCID: PMC8874744 DOI: 10.1007/s11042-022-12082-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 11/23/2021] [Accepted: 01/04/2022] [Indexed: 06/14/2023]
Abstract
Digital medical images contain important information regarding patient's health and very useful for diagnosis. Even a small change in medical images (especially in the region of interest (ROI)) can mislead the doctors/practitioners for deciding further treatment. Therefore, the protection of the images against intentional/unintentional tampering, forgery, filtering, compression and other common signal processing attacks are mandatory. This manuscript presents a multipurpose medical image watermarking scheme to offer copyright/ownership protection, tamper detection/localization (for ROI (region of interest) and different segments of RONI (region of non-interest)), and self-recovery of the ROI with 100% reversibility. Initially, the recovery information of the host image's ROI is compressed using LZW (Lempel-Ziv-Welch) algorithm. Afterwards, the robust watermark is embedded into the host image using a transform domain based embedding mechanism. Further, the 256-bit hash keys are generated using SHA-256 algorithm for the ROI and eight RONI regions (i.e. RONI-1 to RONI-8) of the robust watermarked image. The compressed recovery data and hash keys are combined and then embedded into the segmented RONI region of the robust watermarked image using an LSB replacement based fragile watermarking approach. Experimental results show high imperceptibility, high robustness, perfect tamper detection, significant tamper localization, and perfect recovery of the ROI (100% reversibility). The scheme doesn't need original host or watermark information for the extraction process due to the blind nature. The relative analysis demonstrates the superiority of the proposed scheme over existing schemes.
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Affiliation(s)
- Rishi Sinhal
- Electronics and Communication Engineering, PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur, MP 482005 India
| | - Sachin Sharma
- Research Division, Jagadish Chandra Bose Research Organisation, Gautam Budh Nagar, Uttar Pradesh 203207 India
| | - Irshad Ahmad Ansari
- Electronics and Communication Engineering, PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur, MP 482005 India
| | - Varun Bajaj
- Electronics and Communication Engineering, PDPM Indian Institute of Information Technology Design and Manufacturing, Jabalpur, MP 482005 India
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P. K, B. D. P. Resource-Optimized Selective Image Encryption of Medical Images Using Multiple Chaotic Systems. INTERNATIONAL JOURNAL OF E-COLLABORATION 2022. [DOI: 10.4018/ijec.304379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Securing medical images becomes a major concern, to avoid leaking the confidential data. This problem motivated to develop many low computational complexity methods to encrypt these medical images. In this research work, Block Cipher based Region of interest medical image encryption with multiple maps is proposed. Primarily, Region of Interest (ROI) regions and Region of Background (ROB) are extracted with the help of Laplacian edge detection operator. Further important ROI regions are permuted in a circular fashion with the help of Arnold cat map and angle value. Then permuted ROI part is encrypted using the duffling system and unimportant regions are unchanged. The advantage of proposed work is that encrypt only selected/important part and that will achieve fast execution speed and reduction in computation complexity. The approach presented here enables the storage and transmission of medical image data within an open network. These results show that the security in the proposed method is much better than many chaotic encryption algorithms proposed in the recent times.
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Affiliation(s)
- Kiran P.
- Vidyavardhaka College of Engineering, India
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New geometrically invariant multiple zero-watermarking algorithm for color medical images. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.103007] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
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K. S, K. M, Kora P. Hierarchical multilevel framework using RDWT-QR optimized watermarking in telemedicine. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102688] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Mansour RF, Parah SA. Reversible Data Hiding for Electronic Patient Information Security for Telemedicine Applications. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-021-05716-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Brinda T, Dharma D. Enhancing the compression performance in medical images using a novel hex-directional chain code (Hex DCC) representation. Soft comput 2021. [DOI: 10.1007/s00500-021-05645-0] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Sharma S, Sharma H, Sharma JB, Poonia RC. A secure and robust color image watermarking using nature-inspired intelligence. Neural Comput Appl 2021. [DOI: 10.1007/s00521-020-05634-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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