Pixel Change Research Articles

Classification and Pixel Change Detection of Brain Tumor Using Adam Kookaburra Optimization-Based Shepard Convolutional Neural Network.

The uncommon growth of cells in the brain is termed as brain tumor. To identify chronic nerve problems, like strokes, brain tumors, multiple sclerosis, and dementia, brain magnetic resonance imaging (MRI) is normally utilized. Identifying the tumor on early stage can improve the patient's survival rate. However, it is difficult to identify the exact tumor region with less computational complexity. Also, the tumors can vary significantly in shape, size, and appearance, which complicates the task of correctly classifying tumor types and detecting subtle pixel changes over time. Hence, an Adam kookaburra optimization-based Shepard convolutional neural network (AKO-based Shepard CNN) is established in this study for the classification and pixel change detection of brain tumor. The Adam kookaburra optimization (AKO) is established by integrating the kookaburra optimization algorithm (KOA) and Adam. Here, the pre- and post-operative MRIs are pre-processed and then segmented by U-Net++. The tuning of U-Net++ is done by the bald Border collie firefly optimization algorithm (BBCFO). The bald eagle search (BES), firefly algorithm (FA), and Border collie optimization (BCO) are combined to form the BBCFO. The next operation is the feature extraction and the classification is conducted at last using ShCNN. The AKO is utilized to tune the ShCNN for obtaining effective classification results. Unlike conventional optimization algorithms, AKO offers faster convergence and higher accuracy in classification. The highest negative predictive value (NPV), true negative rate (TNR), true positive rate (TPR), positive predictive value (PPV), and accuracy produced by the AKO-based ShCNN are 89.91%, 92.26%, 93.78%, and 93.60%, respectively, using Brain Images of Tumors for Evaluation database (BITE).

Hybrid Encryption using Advanced Encryption Standard and Arnold Scrambling for 3D Color Images

Digital security ensuring the confidentiality and integrity of visual data remains a paramount challenge. The escalating sophistication of cyber threats necessitates robust encryption methods to safeguard sensitive information from unauthorized access and manipulation. Despite the development of various encryption techniques, inherent vulnerabilities exist within conventional methods that can be exploited by attackers. Therefore, this research aims to investigate the effectiveness of the combined approach of Arnold Scrambling and Advanced Encryption Standard (AES) in mitigating these vulnerabilities and providing a more secure solution. The primary goal of this research is to enhance the security of digital images by mitigating vulnerabilities associated with conventional encryption methods. Arnold Scrambling introduces chaotic mapping to disperse pixel values, while Advanced Encryption Standard (AES) provides robust cryptographic strength through its substitution-permutation network. By combining these methods in an ensemble fashion, the encryption process achieves heightened resilience against various cryptographic attacks. The proposed methodology was evaluated by using standard metrics including Unified Average Changing Intensity (UACI), Number of Pixels Change Rate (NPCR), and entropy analysis. Results indicate consistent performance across multiple test images, namely: Lena, Mandrill, Cameraman, and Plane with Unified Average Changing Intensity (UACI) averaging 33.6% and Number of Pixels Change Rate (NPCR) nearing 99.8%. Entropy values approached maximum, affirming the efficacy of the encryption in generating highly randomized outputs.

A sector fast encryption algorithm for color images based on one-dimensional composite sinusoidal chaos map.

Images are important information carriers in our lives, and images should be secure when transmitted and stored. Image encryption algorithms based on chaos theory emerge in endlessly. Based on previous various chaotic image fast encryption algorithms, this paper proposes a color image sector fast encryption algorithm based on one-dimensional composite sinusoidal chaotic mapping. The main purpose of this algorithm is to improve the encryption and decryption speed of color images and improve the efficiency of image encryption in the big data era. First, four basic chaos maps are combined in pairs and added with sine operations. Six one-dimensional composite sinusoidal chaos maps (CSCM) were obtained. Secondly, select the two best chaotic mappings LCS and SCS. The randomness of these two chaotic mappings was verified through Lyapunov index and NIST SP 800-22 randomness tests. Thirdly, the encryption process is carried out according to the shape of a traditional Chinese fan, and the diffusion and scrambling of each pixel of the image are performed in parallel. This greatly improves encryption speed. When diffusing, changing the value of one pixel can affect the values of multiple subsequent pixels. When scrambling, each pixel changes position with the three pixels before it according to the chaotic sequence. Finally, through many experiments, it is proved that the image encryption algorithm not only greatly improves the encryption and decryption speed, but also improves various indexes. The key space reached 2192, the average information entropy was 7.9994, the average NPCR was 99.6172, and the average UACI was 33.4646. The algorithm can also resist some common attacks and accidents, such as exhaustion attack, differential attack, noise attack, information loss and so on.

Stream ciphers for digital image transactions by learning quantum true random numbers

Abstract The digital economy drives a surge in online digital image transactions, increasing the risk of data breaches due to extensive image file transmission. Stream ciphers, known for their efficiency compared to block ciphers, have emerged as a preferred choice for encrypting images in such transactions to safeguard transmitted data. Nevertheless, traditional stream cipher algorithms face diverse security threats. To address this challenge, efforts have been devoted to generating stream ciphers by generative adversarial networks (GANs) transforming input style into random patterns. Regrettably, these ciphers face issues in key sensitivity, randomness, and style transformation failures. Quantum true random numbers offer a potential solution but are costly to deploy. To handle this dilemma, we design stream ciphers relied on a neural network random number generator (RNG) using quantum true random numbers for training least squares GANs. Specifically, two fully-connected layers are incorporated into the RNG, avoiding the defects of style transformation in existing GANs-based stream ciphers. Besides, a random number calculation formula is employed to ensure that each decimal place output by the generator contributes to the computation of the random numbers. By doing so, the randomness of GANs is enhanced and the deployment of costly quantum devices is avoided. Experiments reveal that the information entropy of our generated images reaches to 7.9991, the adjacent pixel correlation coefficient of the ciphertext attains -0.0015, the Number of Pixel Change Rate and Unified Average Changing Intensity achieve 99.62% and 33.52%, respectively. These results demonstrate that the designed RNG facilitates randomness, whilst having secure properties applied in stream ciphers.

Satellite image encryption using amalgamation of randomized three chaotic maps and DNA encoding

Abstract Image encryption is essential for securing sensitive visual data, protecting privacy, and making certain that only authorized users are able to access the required content. It prevents unauthorized access, tampering, and misuse of images, which is crucial for confidential and secure communications. In fields like healthcare, military, satellite communication, and financial services, encrypted images safeguard against data breaches and cyber threats. As digital images are easily shared and stored, encryption contributes to the integrity maintenance, and privacy of visual data. Motivated by these requirements, we have developed a satellite image encryption scheme that employs a novel 1-D Cosine Sinusoidal Chaotic (1DCSC) map, and two earlier proposed Sine-Tangent Chaotic (STC) and Improved Cosine Fractional Chaotic (ICFCM) maps, in conjunction with Deoxyribonucleic Acid (DNA) operations. The proposed scheme encrypts a given satellite image in four steps. In the initial step, three keys (K1, K2, and K3) are created utilizing a 384-bit shared key and a 128-bit initial vector. In step two, three different chaotic sequences are produced using 1DCSC, STC, and ICFCM maps, which are chosen randomly to encrypt three red, blue or green different components of the given input image. In step three, these three chaotic sequences and the three components of the input image are DNA encoded. In the final step, the green, red, and blue cipher images are created by employing the DNA XOR based diffusion operation between the color components of the DNA-encoded image and DNA encoded chaotic sequences. The proposed scheme obtains entropy value 7.9997, Unified Average Changing Intensity (UACI) value 33.32, and Number of Pixels Change Rate (NPCR) value 99.67.

A chaos-based word-wise stream cipher using keyed strong S-Box

Stream ciphers are crucial in the realm of information security. However, many existing stream ciphers suffer from several vulnerabilities, including insufficient security levels and the use of S-boxes with low non-linearity, fixed points, reverse fixed points and short cycles. To address these issues, a robust word-wise stream cipher was proposed that incorporates a keyed strong S-box and a two-dimensional (2D) chaotic map, significantly enhancing overall security. A 2D non-degenerate quadratic map (2D-NDQM) was constructed and evaluated using bifurcation and phase diagrams, Lyapunov exponent, Kolmogorov entropy, sample entropy, correlation dimension, and TestU01. The 2D-NDQM is then utilized to design a keyed strong S-box construction algorithm. Based on this, a word-wise stream cipher leveraging both the 2D-NDQM and the keyed strong S-box was developed. A series of tests were conducted to evaluate the proposed stream cipher's performance. The results were promising, showing a key space of 2179, an average number of bits change rate of 50.0229 %, a unified average changing intensity of 33.4402 %, and a number of pixels change rate of 99.6671 %. Additional metrics such as Hamming distance, linear complexity, and Shannon entropy also closely aligned with ideal values, confirming the security and feasibility of the proposed algorithm.

Attacking medical images with minimal noise: exploiting vulnerabilities in medical deep-learning systems.

A change in the output of deep neural networks (DNNs) via the perturbation of a few pixels of an image is referred to as an adversarial attack, and these perturbed images are known as adversarial samples. This study examined strategies for compromising the integrity of DNNs under stringent conditions, specifically by inducing the misclassification of medical images of disease with minimal pixel modifications. This study used the following three publicly available datasets: the chest radiograph of emphysema (cxr) dataset, the melanocytic lesion (derm) dataset, and the Kaggle diabetic retinopathy (dr) dataset. To attack the medical images, we proposed a method termed decrease group differential evolution (DGDE) for generating adversarial images. Under this method, a noise matrix of the same size as the input image is first used to attack the image sample several times until the initial adversarial perturbation s0 is obtained. Next, a subset s1 is randomly picked from the initial adversarial perturbation s0 that is still able to cause the samples to be misclassified by the classifier. A new subset s2 is subsequently randomly selected from the adversarial perturbation subset s1, which can still cause the adversarial samples to be misclassified by the classifier. Finally, the adversarial perturbation subset sn with the minimum number of elements is obtained by continuous reduction of the number of perturbed pixels. In this study, the DGDE method was used to attack the images of the cxr dataset, the derm dataset, and the dr dataset; and the minimum number of pixels required to be considered a successful attack was 11, 7, and 7, respectively, while the maximum number of pixel changes was 55, 35, and 21, respectively. The average number of pixel changes was 30, 18, and 11, respectively, in the cxr dataset, the derm dataset, and the dr dataset, respectively, while the percentages of the average number of pixel changes among the total number of pixels of the image were 0.0598%, 0.0359%, and 0.0219, respectively. Unlike the traditional differential evolution (DE) method, the proposed DGDE method modifies fewer pixels to generate adversarial samples by introducing a variable population number and a novel crossover and selection strategy. However, the success rate of the initial attack on different image datasets varied greatly. In future studies, we intend to identify the reasons for this phenomenon and improve the success rate of the initial attack.

A novel approach for encryption and decryption of digital imaging and communications using mathematical modelling in internet of medical things

AbstractThis research introduces an innovative algorithm for the encryption and decryption of greyscale digital imaging and communications in medicine images utilizing Laplace transforms. The proposed method presents a ground breaking approach to image encryption, effectively concealing visual information and ensuring a robust, secure, and reliable encryption process. By leveraging the inherent strengths of Laplace transform, the algorithm guarantees the complete retrieval of the original image without any loss, provided the correct decryption key is used. To thoroughly evaluate the performance of the algorithm, multiple tests were conducted, including extensive statistical analyses and assessments of encryption quality. Key performance metrics were carefully measured, including correlation coefficients and entropy values, which ranged from 7.89 to 7.99. Additionally, the algorithm's effectiveness was demonstrated through peak signal‐to‐noise ratio values, which spanned from 7.597 to 9.915, indicating the degree of similarity between the original and encrypted images. Furthermore, the number of pixels change rate values, ranging from 99.519241 to 99.609375, highlighted the algorithm's ability to produce significantly different encrypted images from the original. The unified average changing intensity values, falling between 35.72345678 and 35.78233456, further underscored the algorithm's proficiency in altering pixel intensities uniformly. Overall, this research offers a significant advancement in the field of image encryption, combining theoretical robustness with practical efficiency.

Comparative Study on Encryption Algorithms for Autism MRI Scan Images

Healthcare centre is the most susceptible sctor. Cyber-attack on medical institutions, such as hospitals and health care centre have increased year after year. Autism health care centre is one of the victims. Autism MRI scan images include sensitive and confidential information about a patient's health and other personal information. Therefore, image encryption is a fundamental aspect of information security, especially in today's digital era where the proliferation of images and the need to protect sensitive visual content are of paramount importance. In order to protect the confidentiality and integrity of images, image encryption techniques work to make sure that only authorised users can view and understand the visual data while prohibiting unauthorised access and modification. The pixel values and spatial properties of the image are hidden using a variety of cryptographic methods and key-based actions. Symmetric encryption algorithm, like Advanced Encryption Standard (AES), which use the same key for encryption and decryption operations, are among the famous image encryption techniques. After that, asymmetric encryption algorithm like Rivest-Shamir-Adleman (RSA), which employ two distinct keys for both encryption and decryption. Next, the research includes a hybridization method of Elliptic Curve Cryptography (ECC) with the Hill Cypher (HC) for image encryption and decryption. There are five different Autism MRI Scan images selected to be used as the input image for encryption process. Then, each image will have two different file format which is PNG and BMP and two different file size which is 256*256px and 512*512px. This research's goal is to compare how well symmetric encryption, asymmetric encryption and hybrid encryption work as three separate image encryption techniques. This can help users rapidly and efficiently speed up the encryption and decryption of images and enhance the security of MRI scan images. By applying differential analysis parameters like Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI) and statistical analysis parameters like Histogram Analysis (HA), the characteristics of the image encryption approach are examined. At the end of the experiment, AES encryption proves to be the best among the three types of encryption with better performance in terms of security with NPCR percentage of 99.72%, UACI percentage of 32.68% and has a higher speed of encryption which only takes 0.0238s but has the second-best distribution in HA of pixel value to frequency compares to ECC with HC.

Image Encryption Using High-Speed Scrambling and ‎Modular Arithmetic

With the enormous growth and advancement of the computer industry, it is critically necessary to transmit a large amount of encrypted multimedia data to prevent third parties from attacking one's privacy or misbehaving. This study proposes a new encryption strategy for preserving original images. It is highly effective and has been shown to be resilient against impulsive noise and loss of data. At first, some random data is entered within the perimeter of the image. Then, three rounds of high-speed scrambling and adaptive diffusion for pixels are carried out to mix the contiguous pixels at random and distribute the newly placed data throughout the image. The suggested encryption method can be used to encrypt original images in any form of representation directly. We offer a particular operation to carry out adaptive diffusion for pixel modulo arithmetic. The results of encryption are evaluated using the histogram, which shows a near-zero correlation with a Number of Pixel Change Rate (NPCR) of 099.624, a Unified Average Change Intensity (UACI) of 033.577, and an average entropy of 7.9993. The proposed method can accomplish much better speed and adapt better to impulse noise and loss of data interference than several conventional and cutting-edge encryption methods.

ViCo: Human visual perception-based contour extraction of Chinese Tang dynasty textile patterns

The study of ancient textile patterns not only enriches historical material but also inspires modern costume art design. Recent developments in computer vision technology provide novel analytical methods for the study of ancient textile patterns. As contours are one of the most significant features in computer vision tasks, contour extraction serves as a prerequisite to accomplishing intelligent understanding and automatic design of textile patterns. In this paper, a perception-consistent contour extraction method (ViCo) that simulates the human visual system from the retina, the lateral geniculate nucleus (LGN), to the V1 area in the cortex is proposed, along with an ancient textile pattern dataset represented by TuanKe in the Chinese Tang dynasty. Experiments were conducted both qualitatively and quantitatively to verify the effectiveness of the proposed method on the TuanKe dataset. Analysis of the results implies the effectiveness of ViCo because the retinal and LGN filter enhances edge information and restrains tiny pixel changes, and the cortex simulation is direction selective. This study is among the initial efforts to employ computer vision technology for ancient textile pattern studies. The study presents a TuanKe dataset and a novel bio-inspired contour extraction method, which has significant historical research importance, fabric art design inspiration, and computer application value.

Global Exponential Synchronization of Delayed Quaternion-Valued Neural Networks via Decomposition and Non-Decomposition Methods and Its Application to Image Encryption

With the rapid advancement of information technology, digital images such as medical images, grayscale images, and color images are widely used, stored, and transmitted. Therefore, protecting this type of information is a critical challenge. Meanwhile, quaternions enable image encryption algorithm (IEA) to be more secure by providing a higher-dimensional mathematical system. Therefore, considering the importance of IEA and quaternions, this paper explores the global exponential synchronization (GES) problem for a class of quaternion-valued neural networks (QVNNs) with discrete time-varying delays. By using Hamilton’s multiplication rules, we first decompose the original QVNNs into equivalent four real-valued neural networks (RVNNs), which avoids non-commutativity difficulties of quaternions. This decomposition method allows the original QVNNs to be studied using their equivalent RVNNs. Then, by utilizing Lyapunov functions and the matrix measure method (MMM), some new sufficient conditions for GES of QVNNs under designed control are derived. In addition, the original QVNNs are examined using the non-decomposition method, and corresponding GES criteria are derived. Furthermore, this paper presents novel results and new insights into GES of QVNNs. Finally, two numerical verifications with simulation results are given to verify the feasibility of the obtained criteria. Based on the considered master–slave QVNNs, a new IEA for color images Mandrill (256 × 256), Lion (512 × 512), Peppers (1024 × 1024) is proposed. In addition, the effectiveness of the proposed IEA is verified by various experimental analysis. The experiment results show that the algorithm has good correlation coefficients (CCs), information entropy (IE) with an average of 7.9988, number of pixels change rate (NPCR) with average of 99.6080%, and unified averaged changed intensity (UACI) with average of 33.4589%; this indicates the efficacy of the proposed IEAs.

Quantum color image encryption: Dual scrambling scheme based on DNA codec and quantum Arnold transform

Abstract In the field of Internet, an image is of great significance to information transmission. Meanwhile, how to ensure and improve its security has become the focus of international research. We combine DNA codec with quantum Arnold transform (QArT) to propose a new double encryption algorithm for quantum color images to improve the security and robustness of image encryption. First, we utilize the biological characteristics of DNA codecs to perform encoding and decoding operations on pixel color information in quantum color images, and achieve pixel-level diffusion. Second, we use QArT to scramble the position information of quantum images and use the operated image as the key matrix for quantum XOR operations. All quantum operations in this paper are reversible, so the decryption operation of the ciphertext image can be realized by the reverse operation of the encryption process. We conduct simulation experiments on encryption and decryption using three color images of “Monkey”, “Flower”, and “House”. The experimental results show that the peak value and correlation of the encrypted images on the histogram have good similarity, and the average normalized pixel change rate (NPCR) of RGB three-channel is 99.61%, the average uniform average change intensity (UACI) is 33.41%, and the average information entropy is about 7.9992. In addition, the robustness of the proposed algorithm is verified by the simulation of noise interference in the actual scenario.

Securing blockchain-enabled smart health care image encryption framework using Tinkerbell Map

IoT enables the emergence and implementation of smart devices to address real-world problems and challenges. Today we are surrounded by various smart devices, such as smart phones, smart homes, smart cars, smart televisions, and smartwatches that assist us in making our lives easier and smoother. IoT is a conglomeration of multiple technologies at various layers to impart the best of ubiquitous and pervasive computing to deliver several benefits in a variety of application sectors such as medicine, agriculture, and industry. In an IoT setting, blockchain technology is used for addressing security challenges and eradicating third-party participation. Utilizing public networks for storing or transmitting health care images poses risks of eavesdropping, data breaches, and unauthorized access. Before uploading medical data to the decentralized network, encryption is required to prevent unauthorized access. The aim of this study is to integrate technologies to ensure transactions are conducted safely and securely. We proposed an IoT-Blockchain system based on chaos encryption scheme using Tinkerbell mapping to ensure medical data integrity and authenticity. The suggested approach is examined to assess performance parameters such as, key space analysis, key sensitivity analysis, Information Entropy (IE), histogram, correlation of adjacent pixels, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index (SSIM). These findings demonstrated that the suggested method is extremely efficient in avoiding security breaches and guaranteeing information integrity.

Enhanced lightweight encryption algorithm based on chaotic systems

In order to improve security and efficiency, this study presents a novel lightweight encryption technique that makes use of chaotic systems. Our method creatively combines the new chaotic KLEIN_64 algorithm with the Keccak-256 hash function, offering a solid basis for producing initial values essential for causing chaotic maps during the encryption process. After a deep validation with rigorous NIST testing, our chaotic pseudo random generator, LAC, exhibits excellent reliability and cryptographic robustness. Furthermore, the complexity of the cryptographic round function is improved by incorporating a second chaotic pseudo random generator that combines chaotic LFSR and Skew Tent Maps, thereby fortifying security measures.Designed with resource-limited applications in mind, our approach ensures that the cryptosystem remains both lightweight and efficient, meeting the stringent constraints typical of such environments. The practical feasibility and performance of our approach are extensively evaluated through FPGA implementation on the Zybo 7Z010 platform. Our implementation achieves a remarkable throughput of 2.820 Gbps while maintaining optimal resource utilization and efficiency. Extensive experimental results confirm the superior security of our cryptosystem, with correlation tests, entropy measurement, and histogram analysis showcasing robustness against statistical attacks. Moreover, the cryptosystem shows little fluctuation in the Unified Average Changing Intensity (UACI) and Non-Linear Pixel Change Rate (NPCR), confirming its resistance to differential attacks. Overall, our technique advances lightweight cryptography by providing a robust and efficient solution to modern cybersecurity challenges. In particular, our approach is well-suited for applications with limited resources, ensuring that security is maintained without compromising on performance or efficiency, thus fulfilling the needs of modern, constrained environments.

Optimized data management with color multiplexing in QR codes

This study integrates colorimetry and computation by identifying their commonalities to develop a novel encryption system centered around color, specifically using QR codes. We propose an approach that multiplexes QR codes of varying colors, each containing distinct information. A key is generated to encapsulate user-specific data and identify the QR code with authentic information. We develop serverless architectures to facilitate rapid encryption and decryption processes. The system’s performance and efficiency are evaluated through two architectures: a sequential system implemented on Google Colab and a distributed system utilizing AWS Lambda serverless architecture. Metrics such as NPCR (Number of Pixels Change Rate), UACI (Unified Average Changing Intensity) and key space analysis, indicative of the system’s robustness, are analyzed according to existing literature. In addition, the cost of this serverless technology is evaluated in comparison to cloud and local. Our findings demonstrate that the serverless architecture offers a viable and efficient solution for coding. The implications of this research extend across various sectors, including defense, healthcare, and everyday digital interactions, presenting a scalable and secure alternative for data encryption and communication.

Enhancing the temporal resolution of water levels from altimetry using D-InSAR: A case study of 10 Swedish Lakes

Lakes provide societies and natural ecosystems with valuable services such as freshwater supply and flood control. Water level changes in lakes reflect their natural responses to climatic and anthropogenic stressors; however, their monitoring is costly due to installation and maintenance requirements. With its advanced hardware and computational capabilities, altimetry has become a popular alternative to conventional in-situ gauging, although subject to the temporal availability of altimetric observations. To further improve the temporal resolution of altimetric measurements, we here combine radar altimetry data with Differential Interferometric Synthetic Aperture Radar (D-InSAR), using ten lakes in Sweden as a testing platform. First, we use Sentinel-1A and Sentinel-1B SAR images to generate consecutive six-day baseline interferograms across 2019. Then, we accumulate the phase change of coherent pixels to construct the time series of InSAR-derived water level anomalies. Finally, we retrieve altimetric observations from Sentinel-3, estimate their mean and standard deviation, and apply them to the D-InSAR standardized anomalies. In this way, we build a water-level time series with more temporal observations. In general, we find a strong agreement between water level estimates from the combination of D-InSAR and Satellite Altimetry (DInSAlt) and in-situ observations in eight lakes (Concordance Correlation Coefficient - CCC >0.8) and moderate agreement in two lakes (CCC >0.57). The applicability of DInSAlt is limited to lakes with suitable conditions for double-bounce scattering, such as the presence of trees or marshes. The accuracy of the water level estimates depends on the quality of the altimetry observations and the lake's width. These findings are important considering the recently launched Surface Water and Ocean Topography (SWOT) satellite, whose capabilities could expand our methodology's geographical applicability and reduce its reliance on ground measurements.

Image encryption algorithm based on multiple chaotic systems and improved Joseph block scrambling

Abstract With the rapid development of digital information technology, images are increasingly used in various fields. To ensure the security of image data, prevent unauthorized tampering and leakage, maintain personal privacy, and protect intellectual property rights, this study proposes an innovative color image encryption algorithm. Initially, the Mersenne Twister algorithm is utilized to generate high-quality pseudo-random numbers, establishing a robust basis for subsequent operations. Subsequently, two distinct chaotic systems, the autonomous non-Hamiltonian chaotic system and the tent-logistic-cosine chaotic mapping, are employed to produce chaotic random sequences. These chaotic sequences are used to control the encoding and decoding process of the DNA, effectively scrambling the image pixels. Furthermore, the complexity of the encryption process is enhanced through improved Joseph block scrambling. Thorough experimental verification, research, and analysis, the average value of the information entropy test data reaches as high as 7.999. Additionally, the average value of the number of pixels change rate (NPCR) test data is 99.6101%, which closely approaches the ideal value of 99.6094%. This algorithm not only guarantees image quality but also substantially raises the difficulty of decryption.

Chaos based image encryption scheme to secure sensitive multimedia content in cloud storage

Cloud computing offers a variety of on-demand services to users and has gained significant prominence in the contemporary era. The security of information stored in cloud data centers has become a central concern, especially for sensitive data like medical images, videos, and multimedia content that require heightened protection when stored in data centers. Cloud users have a responsibility to ensure the security of their data through strategic measures. Focusing on maintaining the privacy of images stored in the cloud, this research introduces an innovative image encryption technique that is based on a modified Skew Tent chaotic map. The suggested modification to the chaotic map includes combining the Skew Tent map with both the sine trigonometric function and perturbation technology. This results in enhanced randomness, more intricate dynamical behavior, a broader chaotic range, and increased sensitivity to initial values in contrast to alternative chaotic maps. The incorporation of this adapted map into a stepwise procedure, involving two rounds of permutation followed by diffusion, effectively accomplishes image data encryption for cloud storage systems. These consecutive operations collectively enhance the encryption method’s randomness and robustness. Through simulations conducted using Cloudsim, the cipher-image exhibits a uniform distribution and achieves a commendable information entropy score of 7.996749. The encryption algorithm significantly reduces correlation coefficients from 1 in the original image to 0 in the encrypted image, while maintaining NPCR (Number of Pixel Change Rate) and UACI (Unified Average Changing Intensity) values within the critical range. Additionally, both theoretical analysis and practical evaluations confirm the algorithm’s resilience against exhaustive, occlusion, and classical attacks. Moreover, extending this encryption framework to video data, a novel video encryption approach is proposed.

A Comprehensive Review on Image Encryption Techniques using Memristor based Chaotic System for Multimedia Application

Image encryption is one of the most widely used techniques for securing images in trusted and unrestricted public media. However, the drawback is weak security in chaotic encryption algorithms, small key space and hardware implementation complexities. Various appropriate encryption algorithms have been carried out for secure image transmission; it becomes a critical issue to protect image integrity, confidentiality and authenticity. To overcome these issues, this article examines several existing memristor-based image encryption algorithms with various chaotic maps. As a result, this research aims to deliver comprehensive literature on image encryption techniques through memristor to help the researchers. Finally, this survey provides all vital literature on the current image encryption techniques with their benefits, drawbacks, developments, and future directions. We also give a general guideline about cryptography. We conclude that all methods are helpful for real-time image encryption. A comparison has been made between many image processing approaches (conventional and memristor) based on metrics such as the number of pixels change rate (NPCR) with the value of 0.9986, histogram, entropy (7.9993), unified average varying intensity (UACI) achieves the value as 0.4996, energy consumption as 0.32pJ and time complexity as 0.9s. The experimental results of various approaches, key sensitivity and statistical analysis revealed that the survey of memristor-based image encryption schemes provides an effective way to secure image transmission in real-time application.