Fragile Watermarking of Medical Image for Content Authentication and Security

Currently in the health environment, medical images are a very crucial and important part of the medical information because of the large amount of information and their disposal two-dimensional. Medical images are stored, transmitted and recovered on the network. The images users await efficient solutions to preserve the quality and protect the integrity of images exchanged. In this context, watermarking medical image has been widely recognized as an appropriate technique to enhance the security, authenticity and content verification. Watermarking image may bring elements of complementary research methods of classical cryptography. The objective of this paper is to develop a method to authenticate medical images to grayscale, detect falsified on these image zones and retrieve the original image using a blind fragile watermarking technique. We propose a method based on the discrete wavelet transform (DWT) for the application of content authentication. In our algorithm, the watermark is embedded into the sub-bands detail coefficient. The subbands coefficients are marked by adding a watermark of the same size as three sub-bands and a comparison of embedding a watermark at vertical (LH), horizontal (HL) and diagonal (HH) details. We tested the proposed algorithm after applying some standard types of attacks and more interesting. The results have been analyzed in terms of imperceptibility and fragility. Tests were conducted on the medical images to grayscale and color size 512 × 512

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

radiomic features for medical images tamper detection by equivalence checking

Abstract Digital medical images are very easy to be modified for illegal purposes. An attacker may perform this act in order to stop a political candidate, sabotage research, commit insurance fraud, perform an act of terrorism, or even commit murder. Between the machine that performs medical scans and the radiologist monitor, medical images pass through different devices: in this chain an attacker can perform its malicious action. In this paper we propose a method aimed to avoid medical images modifications by means of equivalence checking. Magnetic images are represented as finite state automata and equivalence checking is exploited to check whether the medical resource have been subject to illegal modifications

Improved ECG watermarking technique using curvelet transform

Hiding data in electrocardiogram signals are a big challenge due to the embedded information that can hamper the accuracy of disease detection. On the other hand, hiding data into ECG signals provides more security for, and authenticity of, the patient\u27s data. Some recent studies used non-blind watermarking techniques to embed patient information and data of a patient into ECG signals. However, these techniques are not robust against attacks with noise and show a low performance in terms of parameters such as peak signal to noise ratio (PSNR), normalized correlation (NC), mean square error (MSE), percentage residual difference (PRD), bit error rate (BER), structure similarity index measure (SSIM). In this study, an improved blind ECG-watermarking technique is proposed to embed the information of the patient\u27s data into the ECG signals using curvelet transform. The Euclidean distance between every two curvelet coefficients was computed to cluster the curvelet coefficients and after this, data were embedded into the selected clusters. This was an improvement not only in terms of extracting a hidden message from the watermarked ECG signals, but also robust against image-processing attacks. Performance metrics of SSIM, NC, PSNR and BER were used to measure the superiority of presented work. KL divergence and PRD were also used to reveal data hiding in curvelet coefficients of ECG without disturbing the original signal. The simulation results also demonstrated that the clustering method in the curvelet domain provided the best performance-even when the hidden messages were large size

Optimization of medical image steganography using n-decomposition genetic algorithm

Protecting patients' confidential information is a critical concern in medical image steganography. The Least Significant Bits (LSB) technique has been widely used for secure communication. However, it is susceptible to imperceptibility and security risks due to the direct manipulation of pixels, and ASCII patterns present limitations. Consequently, sensitive medical information is subject to loss or alteration. Despite attempts to optimize LSB, these issues persist due to (1) the formulation of the optimization suffering from non-valid implicit constraints, causing inflexibility in reaching optimal embedding, (2) lacking convergence in the searching process, where the message length significantly affects the size of the solution space, and (3) issues of application customizability where different data require more flexibility in controlling the embedding process. To overcome these limitations, this study proposes a technique known as an n-decomposition genetic algorithm. This algorithm uses a variable-length search to identify the best location to embed the secret message by incorporating constraints to avoid local minimum traps. The methodology consists of five main phases: (1) initial investigation, (2) formulating an embedding scheme, (3) constructing a decomposition scheme, (4) integrating the schemes' design into the proposed technique, and (5) evaluating the proposed technique's performance based on parameters using medical datasets from kaggle.com. The proposed technique showed resistance to statistical analysis evaluated using Reversible Statistical (RS) analysis and histogram. It also demonstrated its superiority in imperceptibility and security measured by MSE and PSNR to Chest and Retina datasets (0.0557, 0.0550) and (60.6696, 60.7287), respectively. Still, compared to the results obtained by the proposed technique, the benchmark outperforms the Brain dataset due to the homogeneous nature of the images and the extensive black background. This research has contributed to genetic-based decomposition in medical image steganography and provides a technique that offers improved security without compromising efficiency and convergence. However, further validation is required to determine its effectiveness in real-world applications

Recent Trends in Communication Networks

In recent years there has been many developments in communication technology. This has greatly enhanced the computing power of small handheld resource-constrained mobile devices. Different generations of communication technology have evolved. This had led to new research for communication of large volumes of data in different transmission media and the design of different communication protocols. Another direction of research concerns the secure and error-free communication between the sender and receiver despite the risk of the presence of an eavesdropper. For the communication requirement of a huge amount of multimedia streaming data, a lot of research has been carried out in the design of proper overlay networks. The book addresses new research techniques that have evolved to handle these challenges

Fragile Watermarking of Medical Image for Content Authentication and Security

Currently in the health environment, medical images are a very crucial and important part of the medical information because of the large amount of information and their disposal two-dimensional. Medical images are stored, transmitted and recovered on the network. The images users await efficient solutions to preserve the quality and protect the integrity of images exchanged. In this context, watermarking medical image has been widely recognized as an appropriate technique to enhance the security, authenticity and content verification. Watermarking image may bring elements of complementary research methods of classical cryptography. The objective of this paper is to develop a method to authenticate medical images to grayscale, detect falsified on these image zones and retrieve the original image using a blind fragile watermarking technique. We propose a method based on the discrete wavelet transform (DWT) for the application of content authentication. In our algorithm, the watermark is embedded into the sub-bands detail coefficient. The subbands coefficients are marked by adding a watermark of the same size as three sub-bands and a comparison of embedding a watermark at vertical (LH), horizontal (HL) and diagonal (HH) details. We tested the proposed algorithm after applying some standard types of attacks and more interesting. The results have been analyzed in terms of imperceptibility and fragility. Tests were conducted on the medical images to grayscale and color size 512 × 512