Secure Transmission Of Medical Images Research Articles

Robust zero‐watermarking algorithm based on discrete wavelet transform and daisy descriptors for encrypted medical image

AbstractIn the intricate network environment, the secure transmission of medical images faces challenges such as information leakage and malicious tampering, significantly impacting the accuracy of disease diagnoses by medical professionals. To address this problem, the authors propose a robust feature watermarking algorithm for encrypted medical images based on multi‐stage discrete wavelet transform (DWT), Daisy descriptor, and discrete cosine transform (DCT). The algorithm initially encrypts the original medical image through DWT‐DCT and Logistic mapping. Subsequently, a 3‐stage DWT transformation is applied to the encrypted medical image, with the centre point of the LL3 sub‐band within its low‐frequency component serving as the sampling point. The Daisy descriptor matrix for this point is then computed. Finally, a DCT transformation is performed on the Daisy descriptor matrix, and the low‐frequency portion is processed using the perceptual hashing algorithm to generate a 32‐bit binary feature vector for the medical image. This scheme utilises cryptographic knowledge and zero‐watermarking technique to embed watermarks without modifying medical images and can extract the watermark from test images without the original image, which meets the basic requirements of medical image watermarking. The embedding and extraction of watermarks are accomplished in a mere 0.160 and 0.411s, respectively, with minimal computational overhead. Simulation results demonstrate the robustness of the algorithm against both conventional attacks and geometric attacks, with a notable performance in resisting rotation attacks.

CloudSec: A Lightweight and Agile Approach to Secure Medical Image Transmission in the Cloud Computing Environment

CloudSec: A Lightweight and Agile Approach to Secure Medical Image Transmission in the Cloud Computing Environment

Use of Blockchain technology for the exchange and secure transmission of medical images in the cloud: Systematic Review with Bibliometric Analysis

The research questions focused on emerging challenges, solutions, approaches, encryption methods, architectures, and opportunities related to blockchain technology in medical image sharing, and a total of 17 relevant articles were selected from the Scopus database for analysis. The increasing adoption of cloud computing environments in the medical field has raised concerns about security and privacy risks. Within these concerns, problems were identified regarding secure data storage, since centralized storage systems in the cloud mostly lack security effectiveness. Blockchain technology offers a decentralized and secure infrastructure to address these problems. However, a systematic review is needed to identify the strengths and limitations of this technology in this context, therefore the objective of this review is to provide a critical and updated view of the security aspects of the use of blockchain technology in the exchange of medical images in the cloud. The proposed solutions included practical byzantine fault tolerance algorithms, homomorphic encryption, and the integration of smart contracts, among others. In conclusion, this review provides insight into the application of blockchain technology for the secure sharing of medical images in the cloud and identifies key technologies, challenges, solutions, and opportunities in the field.

Secure transmission of medical images in multi-cloud e-healthcare applications using data hiding scheme

In recent years, medical image transmission using a multi-cloud system has played a significant role in e-Healthcare infrastructure. It allows medical practitioners to easily store, retrieve, and share patients’ medical information across multiple stakeholders. However, multi-cloud image transmission may be vulnerable to multiple security breaches, such as authentication, confidentiality, and security issues. Motivated by these issues, this paper proposes a data-hiding scheme for secure medical image transmission in a multi-cloud environment. The proposed scheme ensures imperceptible robustness and watermark security at a low computational cost. Here, the medical image is divided into a number of shares using Neighbor Mean Interpolation (NMI). To achieve confidentiality, Electronic Patient Healthcare Record (EPHR) is encrypted using Double Scan Pixel Position Shuffling (DSPPS) approach. Then, the encrypted EPHR is divided into shares and embedded in the cover medical image shares. Finally, a minimum of 50% of watermarked image shares are utilized to retrieve the original medical image and encrypted EPHR, consequently reducing multi-cloud latency and computational burden. Experimental results show that the proposed scheme shows high imperceptibility, robustness, and watermark security at a low computational cost. Comparative analysis with some of the recent popular data hiding schemes shows that the proposed scheme has improved imperceptibility and robustness by 10%–15% (approximately) with higher watermark security at a low computational cost.

Region of interest-based medical image encryption technique based on chaotic S-boxes

Block cipher has been one of the most reliable options by which data security is achieved. The strength of block cipher against various attacks is purely dependent on its confusion property, which is gained through the S-Boxes. In recent years, S-Boxes based on chaotic maps have become popular due to their favorable characteristics for cryptography. However, vulnerabilities have been discovered in these constructions, leading to concerns about their reliability. In this research, we first generate dynamic S-Boxes, and then based on the newly generated S-Boxes, a ROI-based medical image encryption scheme is proposed to address the challenges posed by the large size of DICOM images. Rather than encrypting the entire DICOM image, proposed encryption scheme only encrypts the ROI part where the relevant information is present, while leaving the black background unencrypted. This approach reduces the size of the encrypted data and improves the efficiency of the encryption process, while maintaining the privacy and confidentiality of sensitive medical data. The proposed ROI-based medical image encryption scheme is evaluated using standard performance metrics, including encryption speed, image quality tests, correlation-coefficient analysis, randomness of encrypted images, key sensitivity analysis, encryption sensitivity analysis, decryption sensitivity analysis, and resistance against common attacks such as differential and linear attacks. The proposed dynamic S-Box construction technique is evaluated using standard S-Box criteria, which include nonlinearity score, bit independence criterion, strict avalanche criteria, linear approximation probability, and differential approximation probability. Results demonstrate that the proposed technique gains high levels of encryption efficiency and security, making it a fast solution for secure medical image transmission in real-world applications.

Secure medical image transmission using deep neural network in e-health applications.

Recently, medical technologies have developed, and the diagnosis of diseases through medical images has become very important. Medical images often pass through the branches of the network from one end to the other. Hence, high-level security is required. Problems arise due to unauthorized use of data in the image. One of the methods used to secure data in the image is encryption, which is one of the most effective techniques in this field. Confusion and diffusion are the two main steps addressed here. The contribution here is the adaptation of the deep neural network by the weight that has the highest impact on the output, whether it is an intermediate output or a semi-final output in additional to a chaotic system that is not detectable using deep neural network algorithm. The colour and grayscale images were used in the proposed method by dividing the images according to the Region of Interest by the deep neural network algorithm. The algorithm was then used to generate random numbers to randomly create a chaotic system based on the replacement of columns and rows, and randomly distribute the pixels on the designated area. The proposed algorithm evaluated in several ways, and compared with the existing methods to prove the worth of the proposed method.

A Medical Image Encryption Scheme for Secure Fingerprint-Based Authenticated Transmission

Secure transmission of medical images and medical data is essential in healthcare systems, both in telemedicine and AI approaches. The compromise of images and medical data could affect patient privacy and the accuracy of diagnosis. Digital watermarking embeds medical images into a non-significant image before transmission to ensure visual security. However, it is vulnerable to white-box attacks because the embedded medical image can be extracted by an attacker that knows the system’s operation and does not ensure the authenticity of image transmission. A visually secure image encryption scheme for secure fingerprint-based authenticated transmission has been proposed to solve the above issues. The proposed scheme embeds the encrypted medical image, the encrypted physician’s fingerprint, and the patient health record (EHR) into a non-significant image to ensure integrity, authenticity, and confidentiality during the medical image and medical data transmission. A chaotic encryption algorithm based on a permutation key has been used to encrypt the medical image and fingerprint feature vector. A hybrid asymmetric cryptography scheme based on Elliptic Curve Cryptography (ECC) and AES has been implemented to protect the permutation key. Simulations and comparative analysis show that the proposed scheme achieves higher visual security of the encrypted image and higher medical image reconstruction quality than other secure image encryption approaches.

Exploiting Semi-Tensor Product Compressed Sensing and Hybrid Cloud for Secure Medical Image Transmission

With the development of telemedicine diagnosis technology, the collection, storage, and transmission of medical data has become a pivotal problem. To solve these problems, in terms of semi-tensor product compressed sensing (STP-CS) and hybrid cloud, a new medical data transmission framework is presented in this article, which can ensure the efficiency, confidentiality, and verifiability of data transmission. According to the edge detection, the authentication information is embedded into the insignificant area of the medical image to protect the authenticity of the data. STP-CS is utilized to measure and encrypt the medical image, which improves the efficiency of data transmission. In order to make the image data more secure, an adaptive cyclic shift diffusion algorithm based on chaotic sequence is used to effectively diffuse the measurement results. In addition, we also apply encoding for the quantized measurement value to be used as tamper proof authentication. The simulation results prove that the presented medical data transmission scheme can effectively improve the image reconstruction effect, transmission efficiency, and security.

A wavelet-based watermarking for secure medical image transmission in telemedicine application

A wavelet-based watermarking for secure medical image transmission in telemedicine application

Stationary, continuous, and discrete wavelet-based approach for secure medical image transmission

Stationary, continuous, and discrete wavelet-based approach for secure medical image transmission

An efficient audio watermarking scheme with scrambled medical images for secure medical internet of things systems.

In recent times, the security of communication channels between healthcare entities in Medical Internet of Things (MIoT) systems becomes a significant issue to facilitate and guarantee the exchange of medical image and expertise securely. This paper presents an efficient audio watermarking scheme employing professionally Wavelet-based Image Fusion, Arnold transforms, and Singular Value Decomposition (SVD) for the secure transmission of medical images and reports in the MIoT applications. The essential consequence of the proposed scheme is to first syndicate two medical watermarks into a fused watermark to upsurge the payload of the inserted medical images. The fused watermark is then scrambled utilizing Arnold transform. Lastly, the Arnold fused watermark is inserted in the audio signal using the SVD algorithm following converting it into a 2D format. The choice of the Arnold transform for watermark is ascribed to settling robustness that skirmishes respective types of severe attacks. Several assessment metrics such as SNR, LLR, SNRseg, SD, and Cr are used to evaluate the audio watermarked signal and extracted watermarks The results reveal that the proposed audio watermarking scheme increases the capacity with more embedded medical images and security of implanted medical images transmission deprived of affecting the quality of audio signals, especially for IoT-based Telemedicine systems.

Robust Watermarking Method for Secure Transmission of Medical Images in EHR Systems

Robust Watermarking Method for Secure Transmission of Medical Images in EHR Systems

Robust Watermarking Method for Secure Transmission of Medical Images in EHR Systems

Confidentiality of Electronic Health Record (EHR) and privacy are two important security requirements for healthcare systems. Many devices on the EHR network utilize little or no encryption, which makes data in transit vulnerable to exploitative attacks, such as Man-in-the-Middle and other filtration methods. Recently, watermarking algorithms as an efficient response to these requirements is in the underline. In this paper, we present a robust watermarking method conceived as part of an Electronic Health Record platform. In this method a chaotic encryption and blind medical image watermarking technique was incorporated into the system as an authorization mechanism to ensure confidentiality and integrity of electronic health information. We present a hybrid watermarking method based on a combination of discrete wavelet transform (DWT), hessenberg Decomposition (HD), Singular value decomposition (SVD) and an original chaos crypto system based on the Arnold Transform (AT) of Singular Value Decomposition. In order to spread the robustness of our algorithm and provide additional security, an improved SVD-AT embedding and extraction procedure has been used to scramble the EHR data in the preprocessing step of the proposed method. In the process of watermark embedding, an R-level discrete wavelet transform was applied to the host image. The low frequency wavelet coefficients are selected to carry this scrambled-watermark. In extraction process, the stored used plain image is obtained from the trusted authority server to complete the verification process. The receiver should compare the unsigned watermark with the extracted watermark again. The verification can be done before clinical procedures and diagnosis. The proposed watermarking method endures entirety attacks and rightly extracts the hidden watermark without significant degradation in the image quality, thus, when the Peak Signal to Noise Ratio (PSNR) and Normalized Correlation (NC) performance of the proposed algorithm is performed.

A Secure Medical Image Transmission System Based On 2D Logistic Map and Diffie-Hellman Key Exchange Mechanisms

With the tremendous growth of searchable visual media, the content-based medical image retrieval and computer-aided diagnosis systems have become popular in recent years to improve knowledge and provide facilities for radiologists. Medical images transferred throughout public networks demand a mechanism that guarantees image privacy, ownership and source of origin reliability, and image integrity verification. For this reason, secure image retrieval and diagnosis scheme have been given considerable interest due to users’ security concerns. This work proposed a secure framework based on a two-dimensional (2D) chaotic map with Diffie–Hellman key exchange protocols to ensure patient information privacy and security. Consequently, from a security and protection perspective, the objective is to provide a privacy procedure for medical image retrieval systems through image encryption technique combined with a secure key exchange procedure to minimize the possibility of secret key interception by an unauthorized person. Simulation results and security analysis show that the suggested technique could protect images with minimal time complexity and a high level of security while also resisting numerous attacks.

Secure Medical Image Transmission Scheme Using Lorenz’s Attractor Applied in Computer Aided Diagnosis for the Detection of Eye Melanoma

Early detection of diseases is vital for patient recovery. This article explains the design and technical matters of a computer-supported diagnostic system for eye melanoma detection implementing a security approach using chaotic-based encryption to guarantee communication security. The system is intended to provide a diagnosis; it can be applied in a cooperative environment for hospitals or telemedicine and can be extended to detect other types of eye diseases. The introduced method has been tested to assess the secret key, sensitivity, histogram, correlation, Number of Pixel Change Rate (NPCR), Unified Averaged Changed Intensity (UACI), and information entropy analysis. The main contribution is to offer a proposal for a diagnostic aid system for uveal melanoma. Considering the average values for 145 processed images, the results show that near-maximum NPCR values of 0.996 are obtained along with near-safe UACI values of 0.296 and high entropy of 7.954 for the ciphered images. The presented design demonstrates an encryption technique based on chaotic attractors for image transfer through the network. In this article, important theoretical considerations for implementing this system are provided, the requirements and architecture of the system are explained, and the stages in which the diagnosis is carries out are described. Finally, the encryption process is explained and the results and conclusions are presented.

Retraction Note to: A secure medical image transmission algorithm based on binary bits and Arnold map

Retraction Note to: A secure medical image transmission algorithm based on binary bits and Arnold map

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.

Reversible Watermarking Method with Low Distortion for the Secure Transmission of Medical Images

In telemedicine, the realization of reversible watermarking through information security is an emerging research field. However, adding watermarks hinders the distribution of pixels in the cover image because it creates distortions (which lead to an increase in the detection probability). In this article, we introduce a reversible watermarking method that can transmit medical images with minimal distortion and high security. The proposed method selects two adjacent gray pixels whose least significant bit (LSB) is different from the relevant message bit and then calculates the distortion degree. We use the LSB pairing method to embed the secret matrix of patient record into the cover image and exchange pixel values. Experimental results show that the designed method is robust to different attacks and has a high PSNR (peak signal-to-noise ratio) value. The MRI image quality and imperceptibility are verified by embedding a secret matrix of up to 262,688 bits to achieve an average PSNR of 51.657 dB. In addition, the proposed algorithm is tested against the latest technology on standard images, and it is found that the average PSNR of our proposed reversible watermarking technology is higher (i.e., 51.71 dB). Numerical results show that the algorithm can be extended to normal images and medical images.

An efficient watermarking algorithm for medical images

Medical images exchanged over vulnerable networks are sensitive to modifications or tampering which could lead to misdiagnosis, risking the lives of the patients. Therefore, to provide safe and secure transmission of medical images, key security requirements such as authenticity, integrity, and confidentiality must be addressed. In this paper, a novel and efficient algorithm is proposed which provides security services for transmitted medical images in tele-medicine applications. The proposed algorithm employs joint encryption/watermarking procedures to provide the required security services, as well as to provide control access privileges at the receiving side. The algorithm is based on reversible data hiding principles that guarantee the extraction of the hidden data while restoring the original image unaffected. The embedded data is basically two watermarks; a spatial domain watermark and an encrypted domain watermark. The dual watermarks allow the authenticity and integrity of the transmitted medical images to be verified in the spatial and encrypted domains. The proposed algorithm has been extensively evaluated using three performance metrics; embedding capacity, visual image quality, and entropy. The ability of the algorithm to fulfill the security, reversibility, and separability requirements has been validated.

A secure medical image transmission scheme aided by quantum representation

Quantum information and quantum image processing have made immense progress in the most recent years. The proposed paper investigates the proficiency of quantum image encryption in the field of Telehealth which can be built by incorporating quantum block-based spatial transformations. The proposed quantum cryptosystem introduced two levels of security for medical image and its sensitive content. The secrecy of medical image is reinforced by choosing a plain image based initial seed values for encryption. It is found that the suggested quantum block-based scrambling can serve as a novel method for intensifying the privacy-preserving process of medical image. It additionally ensures the medical image integrity by introducing dedicated quantum encryption for ROI based regional data. With a bilateral image transmission perspective, the proposed paper introduced InterPlanetary Features based file system for proficient retrieval of medical images. The quantitative simulation and performance evaluation done on the Cancer Imaging Archive dataset manifests that the suggested quantum-based two-level security for medical images is suitable for reinforcing the confidentiality and privacy-preserving process. It also enlarges the effectiveness of quantum image processing applications in Telemedicine.