Tamper Localisation Using Quantum Fourier Transform Signatures for Medical Image Authentication

Due to the security threats during transmission of any data, exchange of medical images should be done in a protective manner. This has activated the need for confidentiality, authentication, and integrity of medical images. The confidentiality of header data is achieved from medical image exchange standard but for the pixel information it is not achieved. The pixel information achieves authentication and integrity but the header data does not. In this paper, a crypto-oriented algorithm is proposed which offers confidentially, authentication, and integrity for the header and pixel data. AES-GCM (Advanced encryption Standard-Galois counter mode) and Whirlpool hash function are used for achieving these requirements. Simulations are carried out in order to show that confidentiality, authentication, and integrity have been accomplished by the numerical results of entropy, histogram analysis, and correlation.

In this chapter, we will study the quantum Fourier transform and its application in different quantum algorithms. Problems such as factoring an integer into prime numbers or period finding are computationally intractable problems for a classical computer because of the exponentially large number of operations involved. Integer factoring and period finding can be efficiently solved using the quantum phase estimation algorithm that is heavily based on the quantum Fourier transform. Alternately, since quantum phase estimation aims to find the eigenvalue corresponding to an eigenvector of a unitary operator, it is backbone of important algorithms in optimization such as the HHL algorithm (named for Hassim, Harrow, and Lloyd), which serves as the matrix inversion routine in quantum computing. We start this chapter by revising our concepts of the Fourier transform and its discrete counterpart, the discrete Fourier transform, and then move on to the exciting domain of the quantum Fourier transform and the quantum phase estimation algorithm. We follow this up with a discussion and implementation of the few quantum Fourier transform–related algorithms such as factoring a number and period finding. At the end of the chapter, we briefly introduce the basics of group theory with an attempt to explain the hidden subgroup problem and how it relates to several of the Fourier transform–based algorithms.

ABSTRACTThe modern healthcare system demands the safeguarding and dependability of data due to the widespread usage of digital medical records and the exchange of confidential patient information. Preserving the confidentiality and integrity of medical images is of utmost importance as they play a vital role in diagnosing, devising treatment plans, and conducting research. Nevertheless, the inherent digital aspect of these images renders them readily accessible, modifiable, or susceptible to misuse. To mitigate these dangers, the healthcare industry is placing growing significance on the use of robust watermarking systems to safeguard patient data. This article proposes a robust dual watermarking framework that integrates conspicuous and imperceptible watermarks using a combination of Non‐Subsampled Shearlet Transform (NSST), Bi‐dimensional Empirical Mode Decomposition (BMEMD), and Multi‐scale Singular Value Decomposition (MSVD). The approach embeds a visible ownership watermark and a hidden authentication watermark while enhancing security through Redundant Discrete Wavelet Transform (RDWT)‐Randomized Singular Value Decomposition (RSVD)‐based encryption. Experimental evaluation on a subset of 3000 images from the NIH Chest x‐ray dataset demonstrates strong robustness against various attacks such as Gaussian noise, JPEG compression, and blurring. The proposed method achieves a 47.49% improvement in Normalized Correlation (NC) over existing techniques and maintains high visual quality, as indicated by PSNR of 71.4742 dB and SSIM of 0.9997. These results highlight the method's potential for secure medical image transmission and authentication compared to existing watermarking schemes.

In this work, an attempt is made to improve the delicate fractures identification in X-ray images with the help of a pre-trained ResNet50 included in the multitask schema with added use of quantum Fourier transform (QFT). The aim is to identify and locate fine fractures from millions of X-ray images at the case level to assist physicians in delivering early treatment. To compute the remote dependency relation of each position of continuous X-ray image sections and channels, we proposed a new deep learning based pre-trained method using quantum Fourier transform (QFT) as pre-processing. To address this issue, we integrated a pre-trained ResNet50, which has been previously reported to have a strong capability in feature extraction, with a multitask network and QFT, which were expected to improve the model’s capacity in detecting and identifying subtle fracture patterns. Here, the QFT was performed on the ResNet50 extracted feature maps to enhance the detection sensitivity and to demarcate the faint fractures. By combining our multitask network, ResNet50 for the first-step feature extraction and individual detection and segmentation branches, with the assistance of QFT. We utilized a dataset of 600 X-ray images, sourced from a publicly available database, divided into a training set (500 cases) and a test set (100 cases). In this work, the performance of the proposed models was measured against different pre-trained datasets, which shows our method is performing better than other methods. The significance of global awareness and frequency area when it comes to medical images. Therefore, the proposed scheme of the ResNet50-based multitask network developed with QFT is significantly enhanced for detecting fine fractures in X-ray images and paves the way for the practical application in clinical diagnosis. Future work will be aimed at the fine-tuning of the network’s general structure and the investigation of the impact of adding more clinical data for the purpose of improving detection accuracy.

In the contemporary landscape of digital healthcare, the confidentiality and integrity of medical images have become paramount concerns, necessitating the development of robust security measures. This research endeavors to address these concerns by proposing an innovative image encryption scheme tailored specifically for enhancing medical image security. The proposed scheme integrates a sophisticated blend of symmetric and asymmetric encryption techniques, complemented by a novel key management system, to fortify the protection of medical image data against unauthorized access and malicious tampering. The proposed DNA-based encryption algorithm leverages the unique properties of DNA encoding to securely scramble image data, providing an added layer of protection. By utilizing DNA sequences in the encryption and decryption processes, the scheme achieves a high level of data confusion and diffusion, significantly enhancing security. The efficacy of the proposed encryption scheme is validated through comprehensive experimental evaluations, which demonstrate its proficiency in ensuring data security while maintaining computational efficiency. The scheme's compatibility with existing medical imaging systems is also examined, affirming its seamless integration into contemporary healthcare infrastructures. This research contributes to the advancement of medical image security by proposing an efficient encryption scheme that strikes a balance between stringent security requirements and practical implementation considerations. The primary contributions include the development of a DNA-based encryption algorithm and a novel key management system, both of which significantly enhance the security of medical images. This research contributes to the advancement of medical image security by proposing an efficient encryption scheme that strikes a balance between stringent security requirements and practical implementation considerations. By safeguarding the confidentiality and integrity of medical images, the proposed scheme empowers healthcare providers to uphold patient privacy and trust in the digital age. Experimental results show that this approach ensures robust encryption without compromising image quality, making it suitable for sensitive medical imaging applications.

As medical images are created, displayed, transmitted or stored in a digital form, there has been a growing interest in protecting the medical images against external/internal attackers. In this paper, we propose a dual watermarking method(DWM) to protect medical images in transmission/storage. As the proposed DWM provides both robustness and fragileness with the embedded watermarks, it can guarantee the integrity of the medical image transmitted and/or stored. In DWM, watermarks are carefully embedded avoiding the areas of region of interest(ROI) and the edge of the contents to protect the integrity of the medical image. Based on experimental results, we confirm that our DWM can detect the robust watermark accurately and detect the intentional/unintentional leakage of the transmitted or stored medical image.

Digital Watermarking is the art and science of embedding information in existing digital content for Digital Rights Management (DRM) and authentication. Reversible watermarking is a class of (fragile) digital watermarking that not only authenticates multimedia data content, but also helps to maintain perfect integrity of the original multimedia “cover data”. Watermarking of digital images is a well-known technique that is widely used for securing image contents. A successful watermarking method must be accurate, reversible, resilient, and robust against various attacks. The technical revolution associated with the implementation the development of digital technologies in medicine, has led to the emergence and active development of new directions in many areas of medicine. The authenticity and integrity of medical images has to be protected. Hence in this paper a reversible digital image watermarking technique for integrity and authenticity of MR (Magnetic Resonance) images is presented. Reversible watermarking is recognized as a robust approach to confirm the integrity and authenticity of medical images and to verify that alterations can be detected and tracked back.

Singular Value Decomposition (SVD) is one of the most useful techniques for analyzing data in linear algebra. SVD decomposes a rectangular real or complex matrix into two orthogonal matrices and one diagonal matrix. The proposed Quantum-SVD algorithm interpolates the non-uniform angles in the Fourier domain. The error of the Quantum-SVD approach is some orders lower than the error given by ordinary Quantum Fourier Transformation. Our Quantum-SVD algorithm is a fundamentally novel approach for the computation of the Quantum Fourier Transformation (QFT) of non-uniform states. The presented Quantum-SVD algorithm is based on the singular value decomposition mechanism, and the computation of Quantum Fourier Transformation of non-uniform angles of a quantum system. The Quantum-SVD approach provides advantages in terms of computational structure, being based on QFT and multiplications.

Tribal world of belief and practices has been constructed and surrounded by their parochial perception and action of natural and supernatural entity. They find themselves closely knit with the web of these two entities in every sphere of life. Perception about health and health seeking behaviour of the tribal people obviously is intertwined with the interaction of these two entities. The traditional healers act as the medium between man, nature and supernatural entity and provide spiritual security to the tribal people. The extent of meaningful acceptance of modern medicines and health care facilities among tribal people has been remained a mater of debate among social scientist and policy planners. It has been argued that lack of emotional content and spiritual security in modern health care system caused the failure of the system among the tribal people. The present article is based on a primary field study conducted among the Santhals of Orissa that shows the nature, extent and causes of acceptance of modern and traditional health care system by the tribal group.

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

The authenticity and integrity of medical images in telemedicine has to be protected. Robust reversible watermarking (RRW) algorithms provide copyright protection and the original images can be recovered at the receiver’s end. However, the existing algorithms have limitations in their ability to balance the tradeoff among robustness, imperceptibility, and embedded capacity. Some of them are even not completely reversible. Besides, most medical image watermarking algorithms are not designed for color images. To improve their performance in protecting medical color image information, we propose a novel RRW scheme based on the discrete wavelet transform (DWT). First, the DWT provides a robust solution. Second, the modification of the wavelet domain coefficient guarantees the changes of integer values in the spatial domain and ensures the reversibility of the watermarking scheme. Third, the embedding scheme makes full use of the characteristics of the original image and watermarking. This reduces the modification of the original image and ensures better imperceptibility. Lastly, the selection of the Zernike moments order for geometric correction is optimized to predict attack parameters more accurately by using less information. This enhances the robustness of the proposed scheme against geometric attacks such as rotation and scaling. The proposed scheme is robust against common and geometric attacks and has a high embedding capacity without obvious distortion of the image. The paper contributes towards improving the security of medical images in remote healthcare.

Background: Employee turnover poses a multi-faceted challenge to organizations by undermining productivity, morale, and financial stability while rendering recruitment, onboarding, and training investments wasteful. Traditional machine learning approaches often struggle with class imbalance and lack transparency, limiting actionable insights. This study introduces an Explainable AI (XAI) framework to achieve both high predictive accuracy and interpretability in turnover forecasting. Methods: Two publicly available HR datasets (IBM HR Analytics, Kaggle HR Analytics) were preprocessed with label encoding and MinMax scaling. Class imbalance was addressed via GAN-based synthetic data generation. A three-layer Transformer encoder performed binary classification, and SHapley Additive exPlanations (SHAP) analysis provided both global and local feature attributions. Model performance was evaluated using accuracy, precision, recall, F1 score, and ROC AUC metrics. Results: On the IBM dataset, the Generative Adversarial Network (GAN) Transformer model achieved 92.00% accuracy, 96.67% precision, 87.00% recall, 91.58% F1, and 96.32% ROC AUC. On the Kaggle dataset, it reached 96.95% accuracy, 97.28% precision, 96.60% recall, 96.94% F1, and 99.15% ROC AUC, substantially outperforming classical resampling methods (ROS, SMOTE, ADASYN) and recent literature benchmarks. SHAP explanations highlighted JobSatisfaction, Age, and YearsWithCurrManager as top predictors in IBM and number project, satisfaction level, and time spend company in Kaggle. Conclusion: The proposed GAN Transformer SHAP pipeline delivers state-of-the-art turnover prediction while furnishing transparent, actionable insights for HR decision-makers. Future work should validate generalizability across diverse industries and develop lightweight, real-time implementations.

This paper delves into the exploration and evaluation of diverse triple watermarking embedding techniques specifically tailored for medical imaging. In the healthcare sector, where the confidentiality and integrity of medical images are paramount, implementing robust security measures without compromising image quality is crucial. The study focuses on triple watermarking methods that incorporate three layers of security, aiming to enhance the protection of sensitive medical data while optimizing image compression to facilitate efficient storage and transmission. By conducting comparative analyses and performance evaluations, the paper highlights the effectiveness, resilience against tampering, and compression efficiency of these techniques in medical imaging applications. The outcomes show significant advancements in securing medical images, providing a benchmark for future research and development in medical data protection.

Medical images can be intentionally or unintentionally manipulated both within the secure medical system environment and outside, as images are viewed, extracted and transmitted. Many organisations have invested heavily in Picture Archiving and Communication Systems (PACS), which are intended to facilitate data security. However, it is common for images, and records, to be extracted from these for a wide range of accepted practices, such as external second opinion, transmission to another care provider, patient data request, etc. Therefore, confirming trust within medical imaging workflows has become essential. Digital watermarking has been recognised as a promising approach for ensuring the authenticity and integrity of medical images. Authenticity refers to the ability to identify the information origin and prove that the data relates to the right patient. Integrity means the capacity to ensure that the information has not been altered without authorisation.This paper presents a survey of medical images watermarking and offers an evident scene for concerned researchers by analysing the robustness and limitations of various existing approaches. This includes studying the security levels of medical images within PACS system, clarifying the requirements of medical images watermarking and defining the purposes of watermarking approaches when applied to medical images.

Medical images are widely used in telemedicine, sharing and electronic transmission between hospitals. While enjoying convenience, medical images also face privacy disclosure, illegal copy and malicious tamper, etc. It is highly important to ensure privacy and integrity of medical images. Cogitating the above needs, a reversible medical image watermarking algorithm for privacy protection and integrity authentication is proposed. Firstly, the medical image is divided into the ROI (Region of Interest) and RONI (Region of Non-Interest) based on active contour model. Then, the proposed “Three-Dimensional Watermarks” are generated, including authentication watermark, restoration watermark and privacy watermark, which are produced by the novel Parallel Lattice Hash Function, the proposed Neighborhood Difference Method, and the proposed encryption algorithm, respectively. Moreover, “Double-Layer Reversible Embedding Strategy Based on Difference Expansion” is modified in ROI to improve embedding capacity, and “Histogram Modification Reversible Embedding Strategy of Difference Image” is modified to adaptively acquire four or more peak points, which is more flexible than common algorithms. Experimental results confirm the efficient of the proposed scheme, and demonstrate it not only realizes privacy protection, integrity authentication, reversibility, but also holds the characteristics of higher security, larger capacity and better restoration quality.