A New Image Encryption Algorithm for Grey and Color Medical Images

Introduction: Studies on the security and interoperability of Internet of medical things (IoMT)in health are still scarce. The most important effects of the IoMT on health care are the ability to exchange information and reduce hospitalization and health care costs. The main challenges of the IoMT in healthcare are security and protection of privacy, among which encryption of images is very important in the field of communication and security. The main purpose of this study was to design a secure channel for encrypting and sending medical data. Methods: This study presents a new method for encrypting medical images to preserve patient information by synchronizing two chaotic systems. The use of chaotic signals as carriers of medical images greatly enhances security and greatly reduces the possibility of detection. The idea of synchronization of chaotic systems was presented in multi states with fixed time delay. In this idea, with the help of Lyapunov’s theory of stability, a controller was designed to establish the stability of the closed-loop system. Then, according to the synchronization idea and its error, a chaotic masking method was proposed to encrypt patient images. The simulation and accuracy of the results of the presented method were investigated in MATLAB software version 2020.Results: The simulations were performed on medical color images and CT, MRI, and X-ray images. Encrypted and recovered images were obtained by the proposed system. To evaluate the performance of the proposed method, various criteria including image histogram, signal-to-noise rate, correlation, and entropy of information were evaluated. The results showed the excellent image encrypting efficiency of the proposed method.Conclusion: Using the proposed method, medical images were encrypted based on a secure protocol, and then with satisfactory quality, images were recovered after transmission.

Medical image copyright protection is becoming increasingly relevant as medical images are used more frequently in medical networks and institutions. The traditional embedded watermarking system is inappropriate for medical images since it degrades the original images’ quality. Furthermore, medical-colored image watermarking options are constrained since most medical watermarking systems are built for gray-scale images. This paper proposes a zero-watermarking scheme for medical color image copyright protection based on a chaotic system and Resnet50, which is a convolutional neural network method. The network Resnet50 is used to extract features from the color medical image, and then a logistic Gaussian map is used to scramble these features and scramble the binary image. Finally, an exclusive OR operation is performed (scrambled binary image, scrambled features for the medical color image) to form a zero watermarking. The experimental result proves that our scheme is effective and robust to geometric and common image processing attacks. The BER values of the extracted watermarks are below 0.0039, and the NCC values are above 0.9942, while the average PSNR values of the attacked images are 29.0056 dB. Also, it is superior to other zero-watermark schemes for medical images in terms of robustness to conventional image processing and geometric attacks. Furthermore, the experimental results show that the Resnet50 model outperforms other models in terms of reducing the mean squared errors of the features between the attacked and original image.

In this paper, we propose a novel medical color image digital watermarking algorithm, which can realize watermark capacity, transparency and robustness effective compromise. We encrypt the watermark image by Arnold Transform at first and then take it into a random sequence. The principal component of medical image is divided into some sub-blocks with 4×4 and each block is transformed by DCT. One bit of watermark is embedded by comparing the size of specific DCT coefficients in the adjacent sub-blocks. Watermark can be extracted without the original medical image. The experimental results show that the digital watermarking algorithm has a high transparency and big capacity as well as certain robustness.

In this paper, we propose a novel medical color image digital watermarking algorithm, which can realize watermark capacity, transparency and robustness effective compromise. We encrypt the watermark image by Arnold Transform at first and then take it into a random sequence. The principal component of medical image is divided into some sub-blocks with 4×4 and each block is transformed by DCT. One bit of watermark is embedded by comparing the size of specific DCT coefficients in the adjacent sub-blocks. Watermark can be extracted without the original medical image. The experimental results show that the digital watermarking algorithm has a high transparency and big capacity as well as certain robustness.

The continuous evolution of information technology underscores the growing emphasis on data security. In the realm of medical imaging, various diagnostic images represent the privacy of individuals, and the potential repercussions of their unauthorized disclosure are substantial. Therefore, this study introduces a novel chaotic system (TLCMCML) and employs it to propose a multi-image medical image encryption algorithm. To simultaneously augment security and optimize encryption efficiency, we undertake a dual-pronged approach. Firstly, we identify the regions of interest (ROIs) within individual medical images, subsequently applying an independent scrambling technique based on an odd-even interleaving configuration. Secondly, we integrate all medical images through horizontal concatenation, forming a comprehensive large-scale image, upon which we implement a synchronized bit-level permutation-diffusion encryption mechanism. Following extensive testing, enhancements were verified across various metrics including information entropy analysis, adjacent pixel correlation examination, differential attack simulations, and robustness assessments, thereby attesting to the exceptional encryption efficacy of the proposed algorithm.

Recently, compressive sensing (CS) schemes have been studied as a new compression modality that exploits the sensing matrix in the measurement scheme and the reconstruction scheme to recover the compressed signal. In addition, CS is exploited in medical imaging (MI) to support efficient sampling, compression, transmission, and storage of a large amount of MI. Although CS of MI has been extensively investigated, the effect of color space in CS of MI has not yet been studied in the literature. To fulfill these requirements, this article proposes a novel CS of MI based on hue-saturation value (HSV), using spread spectrum Fourier sampling (SSFS) and sparsity averaging with reweighted analysis (SARA). An HSV loop that performs SSFS is proposed to obtain a compressed signal. Next, HSV–SARA is proposed to reconstruct MI from the compressed signal. A set of color MIs is investigated, such as colonoscopy, magnetic resonance imaging of the brain and eye, and wireless capsule endoscopy images. Experiments were performed to show the superiority of HSV–SARA over benchmark methods in terms of signal-to-noise ratio (SNR), structural similarity (SSIM) index, and measurement rate (MR). The experiments showed that a color MI, with a resolution of pixels, could be compressed by the proposed CS at MR of 0.1, and could be improved in terms of SNR being 15.17% and SSIM being 2.53%. The proposed HSV–SARA can be a solution for color medical image compression and sampling to improve the image acquisition of medical devices.

The security and confidentiality of medical images are of utmost importance due to frequent issues such as leakage, theft, and tampering during transmission and storage, which seriously impact patient privacy. Traditional encryption techniques applied to entire images have proven to be ineffective in guaranteeing timely encryption and preserving the privacy of organ regions separated from the background. In response, this study proposes a specialized and efficient local image encryption algorithm for the medical field. The proposed encryption algorithm focuses on the regions of interest (ROI) within massive medical images. Initially, the Laplacian of Gaussian operator and the outer boundary tracking algorithm are employed to extract the binary image and achieve ROI edge extraction. Subsequently, the image is divided into ROI and ROB (regions outside ROI). The ROI is transformed into a row vector and rearranged using the Lorenz hyperchaotic system. The rearranged sequence is XOR with the random sequence generated by the Henon chaotic map. Next, the encrypted sequence is arranged according to the location of the ROI region and recombined with the unencrypted ROB to obtain the complete encrypted image. Finally, the least significant bit algorithm controlled by the key is used to embed binary image into the encrypted image to ensure lossless decryption of the medical images. Experimental verification demonstrates that the proposed selective encryption algorithm for massive medical images offers relatively ideal security and higher encryption efficiency. This algorithm addresses the privacy concerns and challenges faced in the medical field and contributes to the secure transmission and storage of massive medical images.

Chaos-based encryption has emerged as a promising approach for securing digital images, particularly in sensitive domains such as medical imaging. However, existing chaos-based encryption algorithms often suffer from design flaws and the use of weak chaotic maps that exhibit limited chaotic ranges and vulnerability to signal estimation. These limitations result in insufficient key space, reduced resistance to statistical and differential attacks, and inefficiency in real-time applications. To address these challenges, this study introduces a novel 1D Cosine-Exponential (1DCE) map, a hypersensitive chaotic map characterized by enhanced chaotic behavior and robustness. Leveraging the 1DCE map, we propose a real-time image encryption algorithm, termed DCEIES, which incorporates a cross-row-column encryption strategy to significantly improve both encryption speed and security. The DCEIES algorithm also integrates a novel image sensitivity function that detects any alterations in the plain image, rendering the cryptosystem resistant to differential attacks. While the DCEIES algorithm is versatile and applicable to various types of images, its design is particularly optimized for medical imaging, where high pixel correlations and sensitivity to input changes are critical. Extensive simulations and security analyses demonstrate that the DCEIES algorithm outperforms state-of-the-art encryption algorithms in terms of high security and computational efficiency. The results highlight the potential of the DCEIES algorithm for real-time medical image encryption.

Medical imaging techniques have improved to the point where security has become a basic requirement for all applications to ensure data security and data transmission over the internet. However, clinical images hold personal and sensitive data related to the patients and their disclosure has a negative impact on their right to privacy as well as legal ramifications for hospitals. In this research, a novel deep learning-based key generation network (Deep-KEDI) is designed to produce the secure key used for decrypting and encrypting medical images. Initially, medical images are pre-processed by adding the speckle noise using discrete ripplet transform before encryption and are removed after decryption for more security. In the Deep-KEDI model, the zigzag generative adversarial network (ZZ-GAN) is used as the learning network to generate the secret key. The proposed ZZ-GAN is used for secure encryption by generating three different zigzag patterns (vertical, horizontal, diagonal) of encrypted images with its key. The zigzag cipher uses an XOR operation in both encryption and decryption using the proposed ZZ-GAN. Encrypting the original image requires a secret key generated during encryption. After identification, the encrypted image is decrypted using the generated key to reverse the encryption process. Finally, speckle noise is removed from the encrypted image in order to reconstruct the original image. According to the experiments, the Deep-KEDI model generates secret keys with an information entropy of 7.45 that is particularly suitable for securing medical images.

Optimal algorithm for color medical encryption and compression images based on DNA coding and a hyperchaotic system in the moments

Recently, there is an increasing demand for efficient and secure transreception of medical images in telemedicine applications. Though a fixed spectrum is allocated to each user, most of the time it remains unused by the concerned user. Cognitive Radio (CR) is a technology that utilizes the unused spectrum efficiently by adopting spectrum sensing concept. This paper proposes an efficient and secure transmission of medical images by adopting CR technology and image encryption technique. Firstly, the novel medical image encryption algorithm is proposed to encrypt the DICOM (Digital Imaging and Communications in Medicine) image effectively. Then, the spectrum sensing technique is carried out via Universal Software Radio Peripheral (USRP) to sense the unused frequency band to transmit the encrypted bio signal. The proposed encryption algorithm combines DNA (Deoxyribo Nucleic Acid) sequence operation and chaotic maps to successfully encrypt the DICOM image pixels. Experimental results are done and various analysis such as Unified Average Changing Intensity (UACI), Number of Pixel Changing Rate (NPCR), entropy estimation and chi-square tests are carried out to validate the sternness of the encryption algorithm.

Medical images are regarded as important and sensitive data in the medical informatics systems. For transferring medical images over an insecure network, developing a secure encryption algorithm is necessary. Among the three main properties of security services (i.e., confidentiality, integrity, and availability), the confidentiality is the most essential feature for exchanging medical images among physicians. The Goldreich Goldwasser Halevi (GGH) algorithm can be a good choice for encrypting medical images as both the algorithm and sensitive data are represented by numeric matrices. Additionally, the GGH algorithm does not increase the size of the image and hence, its complexity will remain as simple as O(n2). However, one of the disadvantages of using the GGH algorithm is the Chosen Cipher Text attack. In our strategy, this shortcoming of GGH algorithm has been taken in to consideration and has been improved by applying the padding (i.e., snail tour XORing), before the GGH encryption process. For evaluating their performances, three measurement criteria are considered including (i) Number of Pixels Change Rate (NPCR), (ii) Unified Average Changing Intensity (UACI), and (iii) Avalanche effect. The results on three different sizes of images showed that padding GGH approach has improved UACI, NPCR, and Avalanche by almost 100%, 35%, and 45%, respectively, in comparison to the standard GGH algorithm. Also, the outcomes will make the padding GGH resist against the cipher text, the chosen cipher text, and the statistical attacks. Furthermore, increasing the avalanche effect of more than 50% is a promising achievement in comparison to the increased complexities of the proposed method in terms of encryption and decryption processes.

Joint encryption and compression method not only ensure the uniformity of the algorithm but also can enhance the security of algorithm, so it have very important practical significance.This paper studies the over complete dictionary of DCT and OMP algorithm,uses the two kinds of common chaos mapping and learns China remainder theorem. Then one joint image compression and encryption algorithms using chaos map, sparse decomposition and Chinese remainder theorem is proposed to realize image compression and encryption.The experimental results show that the algorithm realizes information transmission requirements in security and efficiency, and achieves the desired requirements.

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.

The healthcare industry has undergone a transformation due to the widespread use of advanced communication technologies and wireless sensor networks such as the Internet of Medical Things (IoMT), Health Information Exchange Technology (HIET), Internet of Healthcare Things (IoHT) and Health IoT (HIoT). These technologies have led to an increase in the transmission of medical data, particularly medical imaging data, over various wireless communication channels. However, transmitting high-quality color medical images over insecure internet channels like the Internet and communication networks like 5G presents significant security risks that could threaten patients’ data privacy. Furthermore, this process can also burden the limited bandwidth of the communication channel, leading to delayed data transmission. To address security concerns in healthcare data, researchers have focused a lot of attention on medical image encryption as a means of protecting patient data. This paper presents a color image encryption scheme that integrates multiple encryption techniques, including alternate quantum random walks, controlled Rubik’s Cube transformations, and the integration of the Elliptic Curve Cryptosystem with Hill Cipher (ECCHC). The proposed scheme divides various plaintext images by creating a regular cube by layering planes of a fixed size. Each plane is rotated in an anticlockwise direction, followed by row, column and face swapping, and then DNA encoding is performed. The image cube encoded with DNA is combined with the chaotic cube through DNA addition, and a couple of random DNA sequences are chosen for DNA mutation. After undergoing DNA mutation, the encoded cube is then decoded using DNA. The proposed method has the theoretical capability of encrypting 2D images of unlimited size and number by utilizing an infinitely large cube. The proposed image encryption scheme has been rigorously tested through various experimental simulations and cyberattack analysis, which shows the efficiency and reliability of the proposed encryption scheme.