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Mahmoud Magdy

Bio: Mahmoud Magdy is an academic researcher. The author has contributed to research in topics: Computer science & Steganography. The author has an hindex of 2, co-authored 3 publications receiving 26 citations.

Papers
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Journal ArticleDOI
TL;DR: In this article , the authors present a detailed discussion of different types of medical images and the attacks that may affect medical image transmission and present an in-depth overview of security techniques, such as cryptography, steganography, and watermarking.
Abstract: Recently, there has been a rapid growth in the utilization of medical images in telemedicine applications. The authors in this paper presented a detailed discussion of different types of medical images and the attacks that may affect medical image transmission. This survey paper summarizes existing medical data security approaches and the different challenges associated with them. An in-depth overview of security techniques, such as cryptography, steganography, and watermarking are introduced with a full survey of recent research. The objective of the paper is to summarize and assess the different algorithms of each approach based on different parameters such as PSNR, MSE, BER, and NC.

16 citations

Journal ArticleDOI
TL;DR: Fast multiple zero-watermarking methods based on Multi-channel Fractional Legendre Fourier moments (MFrLFMs) for medical image security and copyright protection in IoMT applications without deforming the original medical images are presented.
Abstract: Internet of Medical Things (IoMT) plays a vital role in healthcare systems to increase electronic devices accuracy, reliability, and productivity. This paper presents fast multiple zero-watermarking methods based on Multi-channel Fractional Legendre Fourier moments (MFrLFMs) for medical image security and copyright protection in IoMT applications without deforming the original medical images. The MFrLFMs are utilized due to their high accuracy, numerical stability, geometric invariances, and high resistance to various attacks. Based on the most significant features generated from MFrLFMs, after scrambling using two-dimensional Discrete Henon Map, then XORed with binary scrambled watermark to construct owner share. The proposed watermarking method is implemented using a low-cost Raspberry Pi Linux microprocessor, which ensures the suitability of medical devices in the IoMT environment. We evaluated the robustness of the proposed algorithm against different geometric and common signal processing attacks using various medical images. The proposed method gives better BER, NC, and SSIM values than existing methods.

10 citations

Proceedings ArticleDOI
09 Mar 2022
TL;DR: This survey paper summarizes existing image security approaches, their merits and demerits, and the area of future work and presents a brief discussion of different multimedia data, as well as the attacks that affect the image transmission.
Abstract: In recent years due to the observed growth of online multimedia applications, communication, and computer technologies, image security has been an essential demand. This survey paper summarizes existing image security approaches, their merits and demerits, and the area of future work. Along with the survey, the authors present a brief discussion of different multimedia data, as well as the attacks that affect the image transmission, general concepts of multimedia security, primary requirements, and recent applications. Multimedia security is classified into cryptography and data hiding techniques, including digital watermarking, steganography, and hybridization. Recent research work for cryptography techniques is discussed in the following sections.

Cited by
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Journal ArticleDOI
TL;DR: In this paper , the octonion Hahn moments (OHMs) are introduced for color stereo image processing and a modified version of 2D Henon map is presented for potential deployment in the field of information security.

6 citations

Journal ArticleDOI
27 Jul 2022-Sensors
TL;DR: This paper derived multi-channel Gaussian–Hermite moments of fractional-order (MFrGHMs) and used a kernel-based method for the highly accurate computation of MFr GHMs to solve the computation issue and constructed image features that are accurate and robust.
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.

6 citations

Journal ArticleDOI
TL;DR: In this paper , a hybrid machine learning approach was used to classify breast cancer images in a novel manner with the help of a newly devised algorithm that is conceptually more sound as compared to already existing algorithms.
Abstract: Medical imaging is the process of visualizing the diseased part, inside the patient’s body, with the aid of images. The field of medical imaging depends on several disciplines of science and technology, including physics, biological sciences, engineering, artificial intelligence and mathematics. These disciplines contribute in designing the imaging devices, installation of the devices and the collection and analysis of the images for better understanding and future forecasting of the disease prognosis and prevention. In this manuscript, medical images are analyzed with the aid of the a new hybrid machine learning approach, where the breast cancer images are studied in a novel manner with the help of a newly devised algorithm that is conceptually more sound as compared to already existing algorithms. Step by step stages are followed by the algorithm to process, filter, segment, statistically analyze and to classify the medical images. The results from different classification tools are compared in a novel manner, inspired from the explainable artificial intelligence tools for classification. The algorithm devised during this research can serve as a useful tool, in the evolving field of particle - physics -imaging.

5 citations

Journal ArticleDOI
TL;DR: The decrypted elemental image array (EIA) can be reconstructed into a full-color and full-parallax 3D image using the display device, which can be visually displayed to doctors so that they can observe from different angles to design accurate treatment plans and improve the level of medical treatment.
Abstract: This Letter proposes a selective encryption scheme for three-dimensional (3D) medical images using light-field imaging and two-dimensional (2D) Moore cellular automata (MCA). We first utilize convolutional neural networks (CNNs) to obtain the saliency of each elemental image (EI) originating from a 3D medical image with different viewpoints, and successfully extract the region of interest (ROI) in each EI. In addition, we use 2D MCA with balanced rule to encrypt the ROI of each EI. Finally, the decrypted elemental image array (EIA) can be reconstructed into a full-color and full-parallax 3D image using the display device, which can be visually displayed to doctors so that they can observe from different angles to design accurate treatment plans and improve the level of medical treatment. Our work also requires no preprocessing of 3D images, which is more efficient than the method of using slices for encryption.

2 citations