Enhanced security framework for medical data embedding based on octonionic steganographic transforms and FPGA-accelerated integrity verification

This article discusses an approach for both authentication of medical images and confidentiality for the related textual data in an online medical application paradigm. The image authentication is achieved in a soft manner through a feature-based digital signature while the confidentiality of the related patient information is achieved through reversible data hiding. The selected features are robust towards geometric transformations, while fragile towards texture alterations that are characteristic of medical images. The processing scheme is done in a block by block basis to permit the localization of tampered image’s regions. The effectiveness of the scheme, proven through experiments on a sample of medical images, enables us to argue that implementing mechanisms lying on this approach will help to maintain personal patient privacy and medical image integrity.

The growing adoption of telemedicine necessitates robust security measures for medical images during transmission. This paper proposes a novel blind watermarking system for medical images that utilizes both image gradients and the Discrete Wavelet Transform (DWT). Image gradients, acting as spatial derivatives, provide a “topological map” of the image, aiding in the identification of areas susceptible to disruption. The DWT, with its multi-resolution analysis, offers a favorable balance between robustness and imperceptibility. The proposed method embeds the watermark within the low–low band (LL) of the DWT-decomposed image, specifically in 3 × 3 block regions selected based on gradient information. The mathematical relationships between the gradient’s direction and magnitude are employed to extract the corresponding blocks and their codes adequately. These codes are then XORed with the watermark and embedded into the chosen blocks using the least significant bit (LSB) technique. Extensive experimentation on a medical image dataset evaluates the system’s performance against some attacks like filtering, noise, and scaling. The results demonstrate the efficacy of the proposed approach in hiding information while ensuring the security and integrity of watermarked medical images.

Medical image security is possible using digital watermarking techniques. Important information is included in a host medical image in order to provide integrity, consistency, and authentication in the healthcare information system. This paper introduces a proposed method for embedding invisible watermarking in the 3D medical image. The cover medical image used is DICOM which consists of a number of slices, each one representing a sense, firstly must separate the ROI (Region of Interest) and NROI (Not Region Of Interest) for each slice, the separation process performed by the particular person who selected by hand the ROI. The embedding process is based on a key generated from Arnold's chaotic map used as the position of a pixel in the slices with the highest saturation for embedding a secret message in the NROI because the ROI contains the information of the ill, so it cannot be modified. The evaluation of the proposed method using PSNR and MSE shows good results according to both requirements of the watermark system, the invisibility and quality of the watermarked medical image where the PSNR value is up to 43.3936 and MSE is up to 0.000041.

Enforcing protection of medical content becomes a major issue of computer security. Since medical contents are more and more widely distributed, it is necessary to develop security mechanism to guarantee their confidentiality, integrity and traceability in an autonomous way. In this context, watermarking has been recently proposed as a complementary mechanism for medical data protection. In this paper, we focus on the verification of medical image integrity through the combination of digital signatures with such a technology, and especially with Reversible Watermarking (RW). RW schemes have been proposed for images of sensitive content for which any modification may affect their interpretation. Whence, we compare several recent RW schemes and discuss their potential use in the framework of an integrity control process in application to different sets of medical images issued from three distinct modalities: Magnetic Resonance Images, Positron Emission Tomography and Ultrasound Imaging. Experimental results with respect to two aspects including data hiding capacity and image quality preservation, show different limitations which depend on the watermark approach but also on image modality specificities.

Abstract—The Internet of Medical Things, smart healthcare systems are becoming ubiquitous in our daily lives. Patients, doctors, and other medical personnel rely on the safe and efficient storage, transmission, and analysis of electronic health records , medical images such as scanning reports X-rays for successful treatment and management of different ailments. A Multi watermarking method is proposed for medical images based on quantum random walk and optimization algorithm. A logo image is used to verifying medical image integrity is embedding in region of interest and text data are embedded in the region of non interest to conceal private hospital and patient informations. This method improves the authenticity and integrity of medical images. A number of experiments are conducted to validate the security ,robustness and capacity of the proposed multi watermarking scheme. Keywords—medical image, quantum random walk, optimization algorithm, Internet of Medical Things

In telemedicine it is a common practice to exchange medical images between hospitals located at distant places through unsecure networks like internet. During this transfer tampers may be introduced intentionally or unintentionally into medical images. In this circumstances authentication and integrity of medical images using watermarking has become a very popular area of research. In this paper we are proposing a Region of Interest (ROI) and Least Significant Bit (LSB) based fragile watermarking technique for tamper detection and recovery of medical images. At first, medical image is divided into ROI and RONI. Later, authentication information is inserted into ROI and recovery information into RONI. To increase embedding capacity in ROI, authentication information is compressed using Run Length Encoding (RLE) technique before inserting into ROI. Experimental results reveal that any tampering to ROI of medical image is identified and recovered without any loss.

Semi-Blind watermarking using convolutional attention-based turtle shell matrix for tamper detection and recovery of medical images

In today’s scenario, medical image security is a broad area of research using digital image processing. Medical image data has the main importance in e-health care systems these days. Also, the use of cloud computing in the health care sector needs medical image security to maintain the medical image safety, security, integrity, confidentiality, etc. So, for this purpose, encryption and techniques of watermarking have been utilized to secure the medical images. This paper gives a DWT- and SVD-based method to secure the medical images and provides a good PSNR value as illustrated in the result.KeywordsImage securityWatermarkingCryptographyElectronics patient recorde-health care

Medical imaging and information management systems require transmission of medical images over the Internet. Many image watermarking techniques have been proposed in recent years to ensure the integrity and authenticity of medical images transferred over insecure networks. In this work, we propose a new medical image watermarking technique to detect tampered regions on medical images with finer accuracy by authenticating 4×4 blocks and without restricting region of interest (ROI) size. The proposed method can mark a 4×4 pixel block if it has even one tampered pixel, while similar methods (which have no ROI size restriction) mark 8×8, 16×16, and 40×40 pixel blocks. Modified difference expansion (MDE) and least significant bit (LSB) embedding techniques are used together first in the literature by the method to embed authentication bits into corresponding blocks. The method uses a small 4×4 window to mark the tampered region. Experimental results show that the proposed method detects tampered regions on medical images with high accuracy and can be used by all medical image modalities. The results also indicate that the method has finer accuracy and no ROI size restriction compared to similar works reported in the literature.

Medical images are transmitted to other remote places through the E-healthcare system. The protection of medical images is very crucial. Medical images need to be protected against any modification which the attacker may do through the unsecured channel. It is necessary for inspecting the integrity of the ROI (Region of Interest) of the received medical image prior to the adoption of any diagnostic decision to avoid a misdiagnosis. This paper aimed to confirm the integrity and authenticity of medical images by combining the hash signature with the watermarking technique based on the frequency domains. At first, the medical image is divided into ROI and RONI (Region of Non-Interest). The SLT and DCT transformations are employed together to extract the essential features set from ROI and then combined with EPR (Electronic Patient Registration) to produce a watermark, and then it is encoded by a chaotic map with a secret key to provide a signature. On the other hand, the RONI is mapping into randomly subblocks based on the linear system, and then the Signature is embedded in the chosen block using DWT. The hash code of the recovered image and EPR will be compared with the extracted watermark for integrity and authenticity. The experimental results demonstrate the watermark’s robustness against many of the more aggressive and geometric signal processing distortions without affecting the quality of the original medical image. In particular, compared with state-of-the-art technology, the proposed algorithm improves the average NC value larger than 0.90 under all types of attacks.

In telemedicine, the authenticity and integrity of medical images must be safeguarded. Copyright protection is provided through robust medical image watermarking (MIW) methods, and the original pictures may be retrieved at the receiver's end. But existing algorithms have limits in terms of balancing the tradeoff between robustness, imperceptibility, and embedded capacity. Aside from that, most MIW algorithms aren't built for color images. This article proposes a novel MIW technique based on the redundant discrete wavelet transform (RDWT) with singular value decomposition (SVD) to increase their performance in preserving medical color picture information. First and foremost, the RDWT -SVD is a reliable solution as compared to the conventional DWT. Second, modifying the wavelet domain coefficient ensures that integer values in the spatial domain change and that the watermarking process is reversible. Finally, the embedding approach makes full advantage of the original image's features and watermarking. The simulation results showed that the proposed method decreases the amount of original picture change and improves imperceptibility as compared to the conventional approaches.

Musculoskeletal disorders such as osteoarthritis, bone fractures, osteoporosis, or ligament injuries are highly prevalent conditions affecting muscles, bones, joints, and adjacent connective tissues. In 2020, they affected over 1.63 billion people worldwide (GBD 2021 Other Musculoskeletal Disorders Collaborators, 2023). Musculoskeletal disorders cause various symptoms such as pain and mobility limitations, and they have a tremendous impact on the quality of life of an individual imposing a large economic burden to a society (Liu et al., 2022;Parto et al., 2023). The underlying pathophysiological processes and clinical manifestations vary depending on the affected tissue and specific disorder.Imaging provides visualization of the anatomy and physiological processes of the body and plays an important role in the diagnostics of musculoskeletal disorders both in clinical practice and research. Conventional radiography and computed tomography (CT) are usually used to assess changes in the mineralized tissues, such as subchondral bones, whereas magnetic resonance imaging (MRI) is best suited for assessing changes in the soft tissues, for example damage in tendons, ligaments, menisci, and cartilage. Novel imaging, image processing, and image analysis techniques have a great potential to enable earlier diagnosis and deeper understanding of musculoskeletal disorders and pathophysiological processes related to them (Kijowski and Fritz 2023) The aim of this Research Topic was to collect articles showcasing applications of novel and innovative imaging, image processing, and image analysis techniques for assessing musculoskeletal disorders and the underlying pathophysiological processes. Although musculoskeletal conditions can affect various joints, and no specific restrictions were placed on this research topic, the articles included in this research topic focused on disorders involving the spine, spinal joints, and associated muscles.

In order to improve the key space of color image encryption algorithm, the sensitivity to the contents of plain images, the robustness against various types of known attacks, and to achieve the tamper location analysis, a novel color image encryption algorithm based on image hashing, six-dimensional (6D) hyperchaotic and dynamic DNA coding is proposed. Firstly, the color image is pre-processed and the hash sequence is extracted by image hashing algorithm which is used as the initial value and control parameter of chaotic system. Secondly, three color channels of the color image RGB are synthesized into a two-dimensional matrix and the pixels replacement are performed by using the improved two-dimensional chaotic map. Finally, a 6D hyperchaotic system is used to generate random sequences for DNA dynamic coding and arithmetic operations of color images, the encrypted image is obtained. The experimental results show that, compared with the existing methods, the proposed algorithm has a large enough key space, better plain-image sensitivity, better statistical and differential characteristics, as well as can resistant various forms of attacks such as noise and cropping, and the tampering image can be tamper-located analyzed, which has good security and strong robustness.

In this paper, we present a new key generation model for image hashing using neural network, which does not embed any data into the content but is able to extract meaningful data from target image. This model trains artificial neural network to assign predefined code and uses this trained artificial neural network weight and the coordinates of the selected feature sub blocks of target image as keys to extract the predefined code. In this model, the observed output signal from the trained neural network is used as image hash value which distinguishes the target image from other images. The proposed method contributes to secure image hashing for content identification without damaging or losing any detailed data of visual images. The proposed method realizes an application for image authentication, image similarity comparison, verification of image integrity and copyright protection of multimedia contents.

The security and privacy of medical images are crucial due to their sensitive nature and the potential for severe consequences from unauthorized modifications, including data breaches and inaccurate diagnoses. This paper introduces a method for lossless medical image retrieval from encrypted images stored on third-party clouds. The proposed approach employs a symmetric integrity-centric image encryption scheme, leveraging multiple chaotic maps and cryptographic hash techniques, to ensure lossless image reconstruction. Medical images are first encrypted by the image owners and converted into hashcodes encapsulating essential features using a deep hashing technique with the ConvNeXt network as the backbone in parallel. To ensure index privacy, these hashcodes are encrypted in a searchable manner. The encrypted medical images, along with a secure index, are subsequently uploaded to cloud storage. Authorized medical image users can request similar medical images for diagnostic purposes by submitting a query image, from which a search trapdoor is generated and sent to the cloud. The retrieval process involves a secure similar image search over the encrypted indexes, followed by decryption along with integrity verification of the retrieved images. The proposed method has been rigorously tested on three standard medical datasets, demonstrating an improvement of 5-20% in retrieval accuracy compared to standard baselines. Formal security analysis and experimental results indicate that the proposed scheme offers enhanced security and retrieval accuracy, making it an effective solution for the encrypted storage and secure retrieval of medical image data.