Medical Image Authentication Research Articles

Medical Image Authentication Using Statistical Correlations

Medical Image Authentication Using Statistical Correlations

High Capacity and Reversible Fragile Watermarking Method for Medical Image Authentication and Patient Data Hiding.

The exchange of medical images and patient data over the internet has attracted considerable attention in the past decade, driven by advancements in communication and health services. However, transferring confidential data through insecure channels, such as the internet, exposes it to potential manipulations and attacks. To ensure the authenticity of medical images while concealing patient data within them, this paper introduces a high-capacity and reversible fragile watermarking model in which an authentication watermark is initially generated from the cover image and merged with the patient's information, photo, and medical report to form the global watermark. This watermark is subsequently encrypted using the chaotic Chen system technique, enhancing the model's security and ensuring patient data confidentiality. The cover image then undergoes a Discrete Fourier Transform (DFT) and the encrypted watermark is inserted into the frequency coefficients using a new embedding technique. The experimental results demonstrate that the proposed method achieves great watermarked image quality, with a PSNR exceeding 113dB and an SSIM close to 1, while maintaining a high embedding capacity of 3 BPP (Bits Per Pixel) and offering perfect reversibility. Furthermore, the proposed model demonstrates high sensitivity to attacks, successfully detecting tampering in all 18 tested attacks, and achieves nearly perfect watermark extraction accuracy, with a Bit Error Rate (BER) of 0.0004%. This high watermark extraction accuracy is crucial in our situation where patient data need to be retrieved from the watermarked images with almost no alteration.

A robust watermarking approach for medical image authentication using dual image and quorum function

To safeguard the identity and copyright of a patient’s medical documents, watermarking strategies are widely used. This work provides a new dual image-based watermarking approach using the quorum function (QF) and AD interpolation technique. AD interpolation is used to create the dual images which helps to increase the embedding capacity. Moreover, the rules for using the QF are designed in such a way, that the original bits are least affected after embedding. As a result, it increases the visual quality of the stego images. A shared secret key has been employed to protect the information hidden in the medical image and to maintain the privacy and confidentiality. The experimental result using PSNR, SSIM, NCC, and EC shows that the suggested technique gives an average PSNR of 68.44 dB and SSIM is close to 0.99 after inserting 786432 watermark bits, which demonstrates the superiority of the scheme over other state-of-the-art schemes.

A reversible-zero watermarking scheme for medical images

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.

Securing Medical Imaging: A Comprehensive Review and Prospective Paths for Image Authentication

Abstract: As medical image authentication systems depend more and more on cutting-edge technologies, the integrity and dependability of diagnostic procedures are seriously threatened by adversarial attacks. This abstract reviews the latest advancements in Deep Learning (DL)-specific image forgery detection techniques, highlighting common splicing and copy-move attacks as well. This survey covers a wide range of methods used in medical image forgery detection and highlights the role that sophisticated models play in maintaining the accuracy of medical images. The study looked at a range of deep learning and machine learning models as well as methods for isolating photos and using Generative Adversarial Networks (GANs) to detect tampering. The results showed that it is possible to reliably recognize intentionally created anomalies in medical imaging. The case studies show that deep learning performs exceptionally well in correctly identifying scans with injected tumours, especially when it comes to the localization of the region of interest. This works well in situations where localization is not feasible, as reduced-negativespace scans show.

ENHANCING MEDICAL DATA SECURITY IN E-HEALTH SYSTEMS USING BIOMETRIC-BASED WATERMARKING

In the field of Electronic Health (e-Health), Electronic Health Records (EHR) are transmitted between health professionals using e-Health systems for cooperative medical practice, medical monitoring, telemedical expertise, and telemedical imaging. Medical images are a crucial component of EHR and are used in various aspects of telemedicine systems such as expertise, consultation, teaching, and research. However, protecting the authenticity and copyrights of medical images is essential to prevent duplication, modification, or unauthorized distribution. This paper proposes a robust medical image copyright protection method that uses patient palm-print template as watermark and Lorenz chaotic map for template concealing and selecting the appropriate embedding positions in medical images. The novelty of the method lies in optimizing the expected number of modifications per pixel of the medical images after being watermarked. Experimental results indicate that this approach has a high performance with a genuine accept rate of 99.86% and can withstand various image processing attacks, including Gaussian noise, compression, and image rotations, while ensuring personal data security during telemedicine data exchange.

Secure Medical Image Authentication Using Zero-Watermarking based on Deep Learning Context Encoder

Secure Medical Image Authentication Using Zero-Watermarking based on Deep Learning Context Encoder

A reversible fragile watermarking technique using fourier transform and Fibonacci Q-matrix for medical image authentication

Image authentication techniques are crucial for a multitude of multimedia applications. When images are transmitted through non-secure channels like the internet, they are vulnerable to unauthorized alterations and manipulations. Therefore, it is essential in sensitive fields, like the medical industry, to employ image authentication techniques to guarantee the integrity and authenticity of transmitted images. In this paper, to address these security issues and to ensure the authenticity of medical images, we present a reversible fragile watermarking model where the watermark is first generated from the cover image using DCT and then encrypted by the Fibonacci Q-matrix technique, enhancing the model’s security. Subsequently, DFT is performed on the host image and the obtained sub-band is segmented into 2 × 2 blocks, the watermark is then embedded within the frequency coefficients using a new embedding technique. Experimental results indicates that the presented model produces a great watermarked image quality, with a PSNR superior to 117 dB, while maintaining an acceptable embedding capacity of 0.25 BPP and high sensitivity to various attacks, as it was tested against 16 different attacks and could detect tampering in all of them. Furthermore, the presented scheme offers nearly perfect reversibility with a PSNR of 324 dB, which is very useful in the medical field where we need images with as little distortion as possible.

L2NLF: a novel linear-to-nonlinear framework for multi-modal medical image registration.

In recent years, deep learning has ushered in significant development in medical image registration, and the method of non-rigid registration using deep neural networks to generate a deformation field has higher accuracy. However, unlike monomodal medical image registration, multimodal medical image registration is a more complex and challenging task. This paper proposes a new linear-to-nonlinear framework (L2NLF) for multimodal medical image registration. The first linear stage is essentially image conversion, which can reduce the difference between two images without changing the authenticity of medical images, thus transforming multimodal registration into monomodal registration. The second nonlinear stage is essentially unsupervised deformable registration based on the deep neural network. In this paper, a brand-new registration network, CrossMorph, is designed, a deep neural network similar to the U-net structure. As the backbone of the encoder, the volume CrossFormer block can better extract local and global information. Booster promotes the reduction of more deep features and shallow features. The qualitative and quantitative experimental results on T1 and T2 data of 240 patients' brains show that L2NLF can achieve excellent registration effect in the image conversion part with very low computation, and it will not change the authenticity of the converted image at all. Compared with the current state-of-the-art registration method, CrossMorph can effectively reduce average surface distance, improve dice score, and improve the deformation field's smoothness. The proposed methods have potential value in clinical application.

Tchebichef moments and bat optimization algorithm-based information hiding and authentication of medical gray level images

Tchebichef moments and bat optimization algorithm-based information hiding and authentication of medical gray level images

An Optimized Medical Image Watermarking Approach for E-Health Applications

Background: In recent years, information and communication technologies have been widely used in the healthcare sector. This development enables E-Health applications to transmit medical data, as well as their sharing and remote access by healthcare professionals. However, due to their sensitivity, medical data in general, and medical images in particular, are vulnerable to a variety of illegitimate attacks. Therefore, suitable security and effective protection are necessary during transmission. Method: In consideration of these challenges, we put forth a security system relying on digital watermarking with the aim of ensuring the integrity and authenticity of medical images. The proposed approach is based on Integer Wavelet Transform as an embedding algorithm; furthermore, Particles Swarm Optimization was employed to select the optimal scaling factor, which allows the system to be compatible with different medical imaging modalities. Results: The experimental results demonstrate that the method provides a high imperceptibility and robustness for both secret watermark and watermarked images. In addition, the proposed scheme performs better for medical images compared with similar watermarking algorithms. Conclusion: As it is suitable for a lossless-data application, IWT is the best choice for medical images integrity. Furthermore, using the PSO algorithm enables the algorithm to be compatible with different medical imaging modalities.

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

“Telemedicine or e-medicine” refers to an electronic communication system that shares medical information from one location to another through the transmission channel. The increase in multimedia data sharing/transmission across the communication channel has raised to an uncontrollable stage. Thus, the transmission of medical reports across the network has increased higher, which has a chance of tampering either intentional or unintentional attacks. In the case of deliberate attacks, tamper detection and localization are the highly required criteria to verify the originality. In such cases, medical image authentication, security, and integrity verification are also essential criteria in telemedicine applications to verify whether the report belongs to the right person or not. To achieve these criteria and to satisfy these requirements Digital watermarking techniques are highly suggested. Much research has been carried out to address this issue, but some drawbacks and limitations still need to be addressed. The objective is to accomplish a Tamper detection, localization, and recovery (TDLR) system with minimal source information at the receiver end with a better payload or embedding capacity. This paper presents a novel Conventional Attention network (CoAtNet) based invariant watermark generation. A two-level embedding is proposed to enhance watermark security, where image tamper localization and recovery information is generated. The primary level of embedding is carried out by the Turtle Shell Data Hiding Algorithm (TSDH), and the secondary level of embedding is carried out by the DWT-SVD transform. The result shows that the proposed model achieves high imperceptibility and tamper detection accuracy of 60.24 and 99.51% respectively. Furthermore, the suggested system performed well in evaluations of tamper detection and image restoration for various malicious attacks against state-of-the-art systems.

Selection of Digital Watermarking Techniques for Medical Image Security by Using the Fuzzy Analytical Hierarchy Process

Background: The watermarking technique is a security algorithm for medical images and the patient's information. Watermarking is used for maintaining the robustness, integrity, confidentiality, authentication, and complexity of medical images. Objective: The selection of medical image watermarking technique is multi-criteria decision-making and an automatic way of algorithm selection for security and privacy. However, it is difficult to select a better watermarking technique through traditional selection techniques. Methods: To deal with this problem, a multicriteria-based fuzzy analytic hierarchy process (FAHP) was proposed. This method is applied for algorithm selection for the security of medical images in healthcare. In this method, we first determined the list of criteria and alternatives, which directly affect the decision of the medical image security algorithm. Then, the proposed method was applied to the criteria and alternatives. Results: We provided the rank according to the obtained weights of the algorithm. Furthermore, the overall results and ranking of the algorithms were also presented in this article. Conclusion: Integrity was found to have the highest weight (0.509) compared to the other criteria. The weight of the other criteria, namely authentication, was 0.165, robustness was 0.151, confidentiality was 0.135, and complexity was 0.038. Thus, in terms of ranking, integrity was reported to be of the highest priority among the five criteria attributes.

Multi Watermarking for Medical Images using Fractal Encoding and Concept of UNET

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.

Watermarking Technique for Verifying Medical Images in IOMT Using Fractal Encoding and Concept of UNET

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.

Watermarking In Medical Images

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

Контроль неизменности медицинских изображений при передаче по каналам связи

Relevance. Modern methods of ophthalmic medical examinations, for example, ultrasound biomicroscopy, optical coherence tomography, axial MR tomography, and so on, allow you to save results, medical images in electronic form. The obtained image can be stored and transmitted via communication channels, which allows doctors to easily exchange information about the patient, since all tests are now in a single electronic patient card, as well as conduct long-distance consultations. Accurate diagnosis requires that ophthalmic medical images be protected from alteration, both intentional and accidental, when stored and transmitted over communication channels. Purpose. The goal of this work is to investigate the possibility of using modern digital watermark methods to control the integrity and immutability of stored and transmitted ophthalmic medical images. Material and methods. This article uses the results of a study of accurate authentication algorithms conducted using specially written software that allows you to attach and extract a digital watermark. Results. The possibility of using existing algorithms for embedding digital watermarks in ophthalmic medical images to control the absence of distortion during their storage and transmission has been investigated. Here is an example of accurate snapshot authentication using a digital watermark. Conclusion. The article considers the possibility of accurate authentication of ophthalmic medical images to control their invariability. State-of-the-art digital watermarking algorithms allow for monitoring the immutability of ophthalmic medical images during storage and transmission. Keywords: digital watermark, ophthalmic medical snapshot, accurate authentication, immutability, embedding algorithm

A Reversible Digital Image Watermarking Technique for Integrity and Authenticity of MR Images

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.

Medical Image Authentication Method Based on the Wavelet Packet and Energy Entropy

The transmission of digital medical information is affected by data compression, noise, scaling, labeling, and other factors. At the same time, medical data may be illegally copied and maliciously tampered with without authorization. Therefore, the copyright protection and integrity authentication of medical information are worthy of attention. In this paper, based on the wavelet packet and energy entropy, a new method of medical image authentication is designed. The proposed method uses the sliding window to measure the energy of the detail information. In the time–frequency data distribution, the local details of the data are mined. The complexity of energy is quantitatively described to highlight the valuable information. Based on the energy weight, the local energy entropy is constructed and normalized. The adjusted entropy value is used as the feature vector of the authentication information. A series of experiments show that the authentication method has good robustness against shearing attacks, median filtering, contrast enhancement, brightness enhancement, salt-and-pepper noise, Gaussian noise, multiplicative noise, image rotation, scaling attacks, sharpening, JPEG compression, and other attacks.

Verifiable medical images for E-healthcare: A novel watermarking approach using robust bit-wise association of self-mutating offsprings of pixels

In the current difficult time of COVID-19 pandemic, social distancing is a common practice adopted by healthy and infected people all over the world. Because of this, electronic healthcare and Telemedicine are coming up a big way. One of the most vital challenges in E-healthcare or telemedicine is the authentication and secure transmission of medical images received by an expert(doctor) at a far-off location from the sender(patient). To address the critical authentication issue, this paper proposes a blind pixel-based self-embedding fragile watermarking approach that is effective enough to localize the tampered region with more than 90% accuracy. The self-embedding approach plays a significant role in fragile watermarking for detecting the tampered region at the receiver side. Most of the self-embedding-based fragile watermarking approaches available in the literature are block-based which has a high False Positive Rate(FPR). In the proposed approach, every pixel of the image dynamically generates eight self-mutating offsprings and gets associated with them tightly at bit level. After a systematic set of procedures on that offsprings with a triple layer of security, a single authentication bit is selected using 16 × 1 multiplexer for each pixel intensity of the image. That is why the proposed approach has a high imperceptibility level (more than 60 dB PSNR). Experimental results confirm the efficacy of the proposed approach in terms of imperceptibility and tamper detection rate.