Image Encryption Research Articles

Facial image encryption scheme based on improved 4-D hyperchaotic system

Facial image encryption scheme based on improved 4-D hyperchaotic system

Denoising Multi-Level Cross-Attention and Contrastive Learning for Chest Radiology Report Generation.

Chest radiology report generation plays a vital role in supporting diagnosis, alleviating physician workload, and reducing the risk of misdiagnosis. However, significant challenges persist: (1) Data bias and background noise in chest images often obscure subtle lesion details, leading models to generate similar reports; (2) the distinct modal spaces of radiology images and reports weaken the semantic correlation between detailed visual lesion features and report sentences; (3) generated reports often lack crucial patient background and disease extent details, impeding report quality and accuracy. To address these challenges, this paper proposes a novel approach for generating chest radiology reports, utilizing denoising multi-level cross-attention and multi-level contrastive. The proposed method first involves sequential encoding of frontal and lateral radiology images into a visual extractor to enhance semantic coherence across image patches and improve visual feature representation. The enhanced visual features are processed through denoising multi-level cross-attention, which effectively suppresses noise and highlights the details of subtle lesions. Secondly, a multi-level contrastive learning module leverages contrastive among images, text, and disease labels to distinguish positive samples from negative ones, thereby strengthening the semantic correlation between detailed visual lesion features and report sentences. Finally, relevant knowledge is incorporated into the report generator to enhance the description of patient lesion details. Comparative experiments were performed against other state-of-the-art methods on the IU-Xray and MIMIC-CXR datasets, demonstrating that the proposed method significantly improves model performance. Additionally, ablation studies confirm that each module contributes to enhancing the quality of generated reports.

Image encryption based on a hyperchaotic hyperbolic optoelectronic oscillator and the Tetris game

In this paper, we propose, to our knowledge, a novel image encryption scheme based on a hyperchaotic hyperbolic optoelectronic oscillator and the Tetris game. We first demonstrate that a hyperbolic optoelectronic oscillator is capable of displaying highly complex dynamical behavior such as hyperchaos in almost the entire range of its control parameter. Later on, leveraging on such hyperchaotic behavior, we perform efficient image encryption. In the image encryption process, we use a simultaneous permutation-diffusion by channel operation. Prior to that, a plain image pixel rewriting operation and a new pixel-channel permutation based on the rank orders of the values in the hyperchaotic sequence generated by the optoelectronic oscillator are performed. The major core of the permutation phase is a new scrambling by extraction and recombination technique, which is inspired by the Tetris game. In the entire encryption process, the hyperchaotic key stream used is made dependent on the plain image by the SHA-256 function. Results demonstrate that the proposed technique is more efficient than some existing ones and is secure enough to resist existing attack strategies.

Hybrid Algorithm (DES-Present) for encryption image color using 2D-Chaotic System

In recent years, image encryption algorithms have been developed to protect against unauthorized persons and maintain the privacy of recipients. Therefore, it is very important to choose the right algorithm and build it in a way that meets security and performance requirements. The DES algorithm is slow in encryption and decryption operations and vulnerable to attacks. There is a lightweight block cipher for devices with limited resources. May be more vulnerable than differential cryptanalysis. To achieve security, the key of the two algorithms (DES-Present) was generated using a two-dimensional chaos equation system. DES is applied twice for four rounds, and within each single round of the DES algorithm, the current lightweight algorithm is executed for only four rounds. The performance evaluation of the proposed algorithm was measured through several metrics, where a PSNR of 8.985941indicates that the image quality did not change after the encryption procedure, as do MSE 8945.715332and MAE 77.625417. The NPCR value of 99.66891% indicates a high degree of accuracy in changing pixel values. In addition, the uniform average change intensity (UACI) of only 22.0046% shows that the algorithm is good at making large changes in pixel intensity. Correlation coefficient test: horizontal 0.002574; diagonal -0.003017; vertical -0.035860. The results showed that the proposed algorithm is good for encrypting color images, which makes it suitable for use in various applications that need security.

Image encryption algorithm combining semi-tensor product compressed sensing and double random-phase encoding

Abstract By combining semi-tensor product compressed sensing (STP-CS) and double random-phase encoding (DRPE) technology, this paper proposes an optical image encryption scheme with information authentication ability. Firstly, the phase information of the plaintext image is extracted by DRPE and quantized to generate the authentication information. Secondly, to compress the storage space and to realize the multiplication of different dimensional matrices, STP-CS is used to compress the plaintext image, and then the measurements are quantized by sigmoid map, and the quantized measurements are embedded into the authentication message. Finally, a random key stream is generated using a 2D infinite collapse map (2D-ICM) to generate chaotic sequences, and the embedding result is used to generate a ciphertext image by the permutation and XOR diffusion method. The receiver side can perform blind authentication of the ciphertext image during the inverse operation using a nonlinear cross-correlation method. The evaluation results indicate that our encryption scheme is more secure and effective.

A novel six-wing chaotic system with line equilibrium and its application in image encryption

A novel six-wing chaotic system with line equilibrium and its application in image encryption

Deep learning-based compression and encryption of CT images for secure telemedicine applications

Deep learning-based compression and encryption of CT images for secure telemedicine applications

Design, implementation of six-dimensional chaotic system and its application in color image encryption

Abstract A six-dimensional chaotic system is constructed by introducing nonlinear functions. Additionally, the system proposed is confirmed through analog circuits and field-programmable gate arrays (FPGA) digital circuits. A double-block encryption algorithm is proposed that combines the improved 2D Logistic mapping with the six-dimensional chaotic system proposed to perform DNA computation on image pixels, achieving a ‘one image, one key’ effect for secure and efficient encryption of images.

CrackCLIP: Adapting Vision-Language Models for Weakly Supervised Crack Segmentation

Weakly supervised crack segmentation aims to create pixel-level crack masks with minimal human annotation, which often only differentiate between crack and normal no-crack patches. This task is crucial for assessing structural integrity and safety in real-world industrial applications, where manually labeling the location of cracks at the pixel level is both labor-intensive and impractical. Addressing the challenges of labeling uncertainty, this paper presents CrackCLIP, a novel approach that leverages language prompts to augment the semantic context and employs the Contrastive Language–Image Pre-Training (CLIP) model to enhance weakly supervised crack segmentation. Initially, a gradient-based class activation map is used to generate pixel-level coarse pseudo-labels from a trained crack patch classifier. The estimated coarse pseudo-labels are utilized to fine-tune additional linear adapters, which are integrated into the frozen image encoders of CLIP to adapt the CLIP model to the specialized task of crack segmentation. Moreover, specific textual prompts are crafted for crack characteristics, which are input into the frozen text encoder of CLIP to extract features encapsulating the semantic essence of the cracks. The final crack segmentation is determined by comparing the similarity between text prompt features and visual patch token features. Comparative experiments on the Crack500, CFD, and DeepCrack datasets demonstrate that the proposed framework outperforms existing weakly supervised crack segmentation methods, and the pre-trained vision-language model exhibits strong potential for crack feature learning, thereby enhancing the overall performance and generalization capabilities of the proposed framework.

A new dual memristor hyperchaotic system: dynamic properties, electronic circuit, and image encryption

A new dual memristor hyperchaotic system: dynamic properties, electronic circuit, and image encryption

Sensitization of Lanthanides with Metal Chalcogenides Nanocrystals: Night Vision Imaging and Security Encryption

AbstractThe sensitization of lanthanide ions by semiconductor nanocrystals offers promising potential for developing luminophores with high quantum yields, promising for light harvesting and imaging applications. This study introduces lanthanide‐doped CdS nanocrystals fabricated through a phosphine‐free method. Lanthanide ions such as Yb3+, Tm3+, Ho3+, and Er3+ are successfully doped into the CdS matrix, resulting in tunable NIR emissions ranging from 950 to 1600 nm. These nanocrystals exhibit visible–NIR emission with a total photoluminescence quantum yield of ≈41 ± 3%. Steady‐state and time‐resolved spectroscopic studies confirm the broadband sensitization of lanthanide ions by the CdS host. Binary ions‐doped CdS nanocrystals (CdS:Yb3+, Er3+/Ho3+/Tm3+) demonstrate both upconversion and downshifting processes within a single system. Furthermore, the NIR emission is enhanced under CdS excitation compared to direct lanthanide sensitization. Time‐resolved photoluminescence and transient absorption studies reveal the efficient energy transfer processes from CdS to the lanthanide dopants. Additionally, the NIR emission in CdS:Ln3+ embedded polymethyl methacrylate nanocomposites remains highly stable even months after fabrication, making them suitable for night vision imaging and security encryption. This study, therefore, advances the development of smart visible‐to‐NIR light sources for optoelectronic applications, including enhanced anti‐counterfeiting measures and night vision imaging.

A novel approach to visual image encryption: 2D hyperchaos, variable Josephus, and 3D diffusion

Abstract With the development of the Internet, image encryption technology has become critical for network security. Traditional methods often suffer from issues such as insufficient chaos, low randomness in key generation, and poor encryption efficiency. To enhance performance, this paper proposes a new encryption algorithm designed to optimize parallel processing and adapt to images of varying sizes and colors. The method begins by using SHA-384 to extract the hash value of the plaintext image, which is then processed to determine the chaotic system’s initial value and block size. The image is padded and divided into blocks for further processing. A novel two-dimensional infinite collapses hyperchaotic map (2D-ICHM) is employed to generate the intra-block scrambling sequence, while n improved variable Joseph traversal sequence is used for inter-block scrambling. After removing the padding, 3D forward and backward shift diffusions, controlled by the 2D-ICHM sequences, are applied to the scrambled image, producing the ciphertext. Simulation results demonstrate that the proposed algorithm outperforms others in terms of entropy, anti-noise resilience, correlation coefficient, robustness, and encryption efficiency.

An encryption algorithm for multiple medical images based on a novel chaotic system and an odd-even separation strategy

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.

Enhancing colour image encryption through parameters optimization of memristive hyperchaotic system with CPSO algorithm and LSAIM

ABSTRACT Particle Swarm Optimization (PSO) is a simple and effective algorithm for optimization. However, it often converges too early and depends heavily on proper parameter settings for good results, especially in complex or high-dimensional search spaces. Therefore, this algorithm is inappropriate for parameter optimization of hyperchaotic systems and may not provide sufficient robustness and security of colour images. This study presents a new colour image encryption based on optimizing a four-dimensional Memristor-based hyperchaotic system, in which the fourteen parameters of the hyperchaotic system are optimized by the Chaotic Particle Swarm Optimization (CPSO) method, in combination with DNA code and a new logistic sine adjusted integrated map (LSAIM). Simulation results have shown that the algorithm achieved a key space of up to 21116, providing sufficient protection against brute-force attacks, with entropy values close to the ideal (7.9994), NPCR of 99.6178%, and UACI of 33.965%, indicating robust security.

Color image encryption scheme based on a single attractor hyperchaotic system and Go rules

Color image encryption scheme based on a single attractor hyperchaotic system and Go rules

Optically transparent dual-band microwave chiral metamaterial based on ITO strips.

In this paper, an optically transparent dual-band microwave chiral metamaterial based on indium tin oxide (ITO) strips is proposed. The rotation angle and length of the three ITO strips on the structural layer can be varied to generate two independent frequency bands in the circular dichroism (CD) spectrum. The maximum CD value is 0.72, and the optical transmittance reaches 64%. The experimental and simulation results are largely in agreement. Moreover, a designed optically transparent 2D microwave image encoder is proposed as a potential application. The proposed device has considerable potential for application in the fields of signal transmission, information coding, and homeland security.

Blockchain-Driven Optimized Chaotic Encryption Scheme for Medical Image Transmission in IoT-Edge Environment

Internet of Things (IoT) is adopted in a wide spectrum of applications in which a vast amount of data are produced and distributed to centralized cloud platforms to deliver various services. It involves smart devices that collect thousands of terabytes of heterogeneous data and deployed this to make instant decision that aids for the better performance and most comfort life. Traditional IoT architecture is heavily centralized, where it stores the most sensitive information that creates the multiple threats and security breaches as the attackers target towards these centralized cloud systems. To improve the security chain in IoT environment, edge computing (EC) was introduced to distribute the applications of IoT at the edge of the communication networks. However, these edge-based IoT are also vulnerable to many threats due to their decentralized and in secured management. Block chain (BC) technology offers a most trusted solution to resolve the security issues in the IoT-Edge computing environment. This research study presents the block chain driven medical image encryption technique using modified honey badger optimization with the ensemble chaotic systems. The proposed block chain framework uses the divergent methods that integrates differential scroll, Hénon chaotic maps and modified honey badger optimization to generate the optimum keys and high secured image data. These high secured data are stored in the block chain, ensuring the image security to be stored in edge nodes. The complete framework was experimented using Ethereum using Ganache API and Python3.19 are utilized as the major programs for designing the varied interfaces of the recommended model. The comprehensive experimentation is undertaken to assess the security strength of the recommended encryption scheme. The evaluation metrics like as NACI, UACI, Entropy and standard verification methods such as NIST standard tests are deployed and analyzed. To prove it security strength, proposed secured BC framework is compared with the wide-variety of secured frameworks. The experimental findings reveal that the suggested framework establishes a more robust and secure environment for image exchange, surpassing the performance of other blockchain-based systems in terms of integrity, robustness and security. Finally, the paper spreads the bright light of advantages in deploying the proposed framework to formulate the most secured environment in the IoT-Edge environment for medical image transmission.

The synchronisation control of fractional 4-D quantum game chaotic map with its application in image encryption

The synchronisation control of fractional 4-D quantum game chaotic map with its application in image encryption

Lightweight image encryption scheme for IoT environment and machine learning-driven robust S-box selection

Lightweight image encryption scheme for IoT environment and machine learning-driven robust S-box selection

Image Encryption Method Based on Three-Dimensional Chaotic Systems and V-Shaped Scrambling.

With the increasing importance of securing images during network transmission, this paper introduces a novel image encryption algorithm that integrates a 3D chaotic system with V-shaped scrambling techniques. The proposed method begins by constructing a unique 3D chaotic system to generate chaotic sequences for encryption. These sequences determine a random starting point for V-shaped scrambling, which facilitates the transformation of image pixels into quaternary numbers. Subsequently, four innovative bit-level scrambling strategies are employed to enhance encryption strength. To further improve randomness, DNA encoding is applied to both the image and chaotic sequences, with chaotic sequences directing crossover and DNA operations. Ciphertext feedback is then utilized to propagate changes across the image, ensuring increased complexity and security. Extensive simulation experiments validate the algorithm's robust encryption performance for grayscale images, yielding uniformly distributed histograms, near-zero correlation values, and an information entropy value of 7.9975, approaching the ideal threshold. The algorithm also features a large key space, providing robust protection against brute force attacks while effectively resisting statistical, differential, noise, and cropping attacks. These results affirm the algorithm's reliability and security for image communication and transmission.