Multiple Chaotic Maps Research Articles

SMedIR: secure medical image retrieval framework with ConvNeXt-based indexing and searchable encryption in the cloud

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.

Harris Hawk optimization driven adaptive image encryption integrating Hilbert vibrational decomposition and chaos

Image encryption is a technique used to protect the confidentiality of digital images. Existing encryption algorithms use chaotic maps for generating random sequences. These sequences purely depend on the fixed initial parameters, which are not adaptive to different input images, thereby limiting their ability to produce the best-possible encrypted image. This paper introduces a new Harris Hawk optimization (HHO) based adaptive image encryption algorithm that integrates Hilbert vibrational decomposition (HVD), and multiple chaotic maps. The new fitness function is specifically designed to optimize the security and robustness of the encrypted image, considering statistical and differential attack parameters (NPCR, UACI, Entropy, and correlation coefficient values). HHO is used to optimize the initial parameters of chaotic maps (Henon map, Duffing map, Lorenz equation) subject to variations in the input image. In the confusion stage, the input image is decomposed into four components of same dimension but decreasing energy using HVD. Subsequently, the block based pixel scrambling is performed on alternate components using the Henon map and Duffing map. In the diffusion stage, the image from the confusion stage undergoes a first level diffusion by bitwise XOR operation with a random image generated from the Lorenz equation. Further, second level of diffusion is achieved with a complex operation generated image from the input image, enhancing its adaptability. Finally, Arnold Cat map having number of iterations determined by the input image is used for final pixel permutation to produces the encrypted image. The optimized initial parameters and input image dependent confusion and diffusion operations enable the encryption system to adapt to changes in the input image, thereby enhancing its resistance against known plain text and known cipher text attacks. Moreover, the integration of HVD with multiple chaotic maps makes the scheme more complex to decrypt with plain text attacks. The performance of proposed algorithm is evaluated on five standard test images in terms of security and quality measures. Experimental results exhibit statistical and differential attack parameters close to ideal values (with Max. Entropy≈7.9996, Min. CC≈ 10−5, Max. NPCR≈99.64, Max. UACI≈33.62). The use of multiple chaotic maps at different stages makes the key space larger. Robustness analysis demonstrates the algorithm's capability in withstanding noise and image enhancement attacks. The comparison with state of the arts metaheuristic based and classical encryption algorithms highlights the superior performance of proposed HHO driven algorithm. The significant improvement in security parameters and overall fitness compared to fixed initial parameters is achieved by optimizing the encryption process.

A Secure Transmission of Digital Images using Multiple Chaotic Maps and Elliptic Curve

A Secure Transmission of Digital Images using Multiple Chaotic Maps and Elliptic Curve

An efficient 32-bit color image encryption technique using multiple chaotic maps and advanced ciphers

In this study, we introduce a refined approach to encrypting 32-bit color images, leveraging the potential of four 1D chaotic maps – the Logistic map, Tent map, Chebyshev map, and Sine map. These chaotic maps intricately populate the four matrices within our encryption system, assigning exclusive integers ranging from 0 to 255. Our proposed methodology employs 16 × 16 matrices to represent the four channels (red, green, blue, and alpha) of a 32-bit color image, strategically utilizing specific grids for channel encryption. The top-left and bottom-right grids facilitate the encryption of the red and alpha channels, respectively, while the top-right and bottom-left grids are employed for encrypting the green and blue channels. The algorithm initiates by extracting decimal values from each pixel in the source image, mapping them to their corresponding positions in the matrices. A subsequent right circular shift operation on each pixel, determined by its row and column coordinates, is performed to prevent the encryption of areas with uniform color. To enhance security further, we employ the Four-square cipher method to encrypt the decimal values of the pixels. In the confusion stage, we apply the Arnold Cat Map transformation to strategically rearrange the position of all pixels, introducing an additional layer of complexity. Rigorous assessments using various security criteria were conducted to evaluate our algorithm's performance against common attacks, yielding consistently excellent results. Our method demonstrated superior outcomes, including a 25 % to 44 % increase in resistance to common attacks compared to existing methods.

MieWC: Medical image encryption using wavelet transform and multiple chaotic maps

AbstractRecently, digital images have been widely used in several applications through advanced wearable devices and networks. Despite several benefits of digital images, such as easy distribution, storage, and reproduction, it is difficult to prevent the issue of identity theft, privacy leakage, and ownership conflicts. We present a wavelet‐based encryption technique, MieWC, to solve the above issues using multiple chaotic keys. This work uses multiple keys for the encryption process, which ensures high security and authenticity. Lorenz and logistic maps are used for chaotic key generation for diffusion and confusion processes. Further, the technique is tested for two different wavelet transforms, including integer wavelet transform (IWT) and discrete wavelet transform, with IWT giving better results. Hence, it is being used for further analysis. The proposed technique is easy to implement and provides high security with improved results. It has low time complexity and high key bit sensitivity. The result analysis further ensures resistance against crop attacks.

Big S-Boxes for Deeply Improved Hill Image Coding

This paper introduces a novel approach to encrypt color images, utilizing multiple chaotic maps and working with any size pixel blocks. The encryption procedure commences by converting the original image into a vector, which is then divided into equal dimension subblocks. These blocks undergo a new Vigenère transformation, employing two large dynamic substitution tables interconnected by an enhanced Hill operation using a large invertible matrix generated from a pseudo-random table, controlled by another binary dynamics table. Additionally, the truncated block undergoes the same encryption process using another pseudo-random invertible matrix. To evaluate the effectiveness and resilience of our algorithm, we conducted numerous simulations on randomly selected images of varying sizes and formats from diverse databases.

Dynamic DNA cryptography-based image encryption scheme using multiple chaotic maps and SHA-256 hash function

In today's world, a substantial number of digital images are conserved and transmitted through diverse online platforms on a daily basis. Preserving the confidentiality of images is a paramount issue in the current digital environment. To safeguard the privacy of digital images on internet platforms, a robust, effective, and reliable encryption technique is required. This paper proposes a robust and efficient image encryption scheme based on a dynamic DNA encoding and DNA operations associated with chaotic maps with simple structures and highly chaotic behavior, such as the Logistic map, the Henon map, and the Lorenz system, to provide substantial security for digital images. Furthermore, the SHA-256 hash technique and zigzag traversal are used in the algorithm to reinforce the system. This paper defines an improved DNA encoding scheme that can encode four bits at once instead of two bits. Besides, unique and randomized keys are generated for each encryption and decryption session. The proposed scheme exhibits several admirable characteristics, including low processing cost, high randomness, large key space, flexible parameter space, high sensitivity to both keys and plaintext, and fast execution speed. As a result, the proposed scheme effectively safeguards sensitive digital images from a broad range of cryptographic attacks. Numerous evaluation results have demonstrated that the proposed algorithm is more secure and efficient than state-of-the-art methods against a huge assortment of cryptographic attacks.

New Variants of the Multi-Verse Optimizer Algorithm Adapting Chaos Theory in Benchmark Optimization

In this work, we present multiple variations of the Multi-verse Optimizer Algorithm (MVO) using chaotic maps, using it in the formation of new solutions. In these new variations of the MVO algorithm, which we call the Fuzzy-Chaotic Multi-verse Optimizer (FCMVO), we use multiple chaotic maps used in the literature to substitute some of the parameters for which the original algorithm used a random value in the formation of new universes or solutions. To implement chaos theory on these new variants, we also use Fuzzy Logic for dynamic parameter adaptation; the first tests are performed only using chaotic maps, and then we merge the use of Fuzzy Logic in each of these cases to analyze the improvement over the Fuzzy MVO. Subsequently, we use only the best-performing chaos maps in a new set of variants for the same cases; after these results, we observe the behavior of the algorithm in different cases. The objective of this study is to compare whether there is a significant improvement over the MVO algorithm using some of the best-performing chaotic maps in conjunction with Fuzzy Logic in benchmark mathematical functions prior to moving on to other case studies.

Combined image encryption and steganography technique for enhanced security using multiple chaotic maps

This paper proposes a novel security algorithm which combines the concepts of Cryptography and Steganography to provide enhanced security to digital images. The proposed algorithm, named CIEST, enciphers the secret image (cryptography) which is hidden into the cover image (steganography) for secured transfer of information. A two-stage encryption process, with one of the stages being DNA encoding is used here. The cover image is also modified before the data embedding phase to improve the effectiveness of the algorithm. Multiple Combined Chaotic maps are utilised in the encryption and embedding stages to improve the randomness of the algorithm. The use of chaotic maps also makes the algorithm more robust to different types of attacks. The CIEST scheme is shown to be robust, offering high embedding capacity with high sensitivity with a near zero correlation value, information entropy value of 7.9972, maximum SSIM of 0.9992, and a maximum PSNR of 56.1071. The experimental results presented establish the immunity of the proposed method against attacks along with the feasibility and effectiveness of the algorithm in real time.

Digital watermarking scheme based on curvelet transform and multiple chaotic maps

The rapid development of digital products brings security issues. Digital watermarking technology is an important means to handle these problems. To enhance the imperceptibility of watermark and locate the possible tampering as well, a digital watermarking scheme based on curvelet transform is presented by combining with multiple chaotic maps. The host image is decomposed into three parts, i.e., coarse layer, detail layer and fine layer, with curvelet transform, and a robust watermark is embedded into the coarse layer for copyright protection of digital products. In addition, an authentication watermark is embedded into the fine layer to detect and locate the illegal changes. Simulation results show that the proposed digital watermarking scheme possesses acceptable robustness and security.

Color Image Encryption Through Chaos and KAA Map

The unprecedented growth in production and exchange of multimedia over unsecured channels is overwhelming mathematicians, scientists and engineers to realize secure and efficient cryptographic algorithms. In this paper, a color image encryption algorithm combining the KAA map with multiple chaotic maps is proposed. The proposed algorithm makes full use of Shannon’s ideas of security, such that image encryption is carried out through bit confusion and diffusion. Confusion is carried out through employing 2 encryption keys. The first key is generated from the 2D Logistic Sine map and a Linear Congruential Generator, while the second key is generated from the Tent map and the Bernoulli map. Diffusion is attained through the use of the KAA map. An elaborate mathematical analysis is carried out to showcase the robustness and efficiency of the proposed algorithm, as well as its resistance to visual, statistical, differential and brute-force attacks. Moreover, the proposed image encryption algorithm is also shown to successfully pass all the tests of the NIST SP 800 suite.

A Chaos‐Based Image Encryption Scheme Is Proposed Using Multiple Chaotic Maps

Everybody wants to maintain solitariness to some extent or entirely in his dealings with other people during different modes of communication. To retain privacy, researchers materialized distinct image encryption algorithms using chaotic maps. Due to their extraordinary features, most researchers employed multidimensional chaotic maps to barricade clandestine information or digital images from potential invaders. Still, multidimensional chaotic maps have many impediments conferred in the literature review. In this paper, we developed a cryptosystem utilizing multiple chaotic maps to mitigate the shortcoming of multidimensional chaotic maps. A distinctive approach is adopted to sire a key stream using a combination of chaotic maps and create a sequence of random integers linked with the pixels of the plain image to shatter the association between neighboring pixels of a plain image. Finally, diffusion is accomplished using the previously diffused pixels at a decimal level. Security and statistical analysis demonstrate that the presented encryption algorithm is robust against well‐known attacks. An ample key space indicates that it is best suited for secure communication.

An Efficient Lightweight Image Encryption Scheme Using Multichaos

With an immense increase in Internet multimedia applications over the past few years, digital content such as digital images are stored and shared over global networks, the probability for information leakage and illegal modifications to the digital content is at high risk. These digital images are transferred using the network bandwidth; therefore, secure encryption schemes facilitate both information security and bandwidth issues. Hence, a state-of-the-art lightweight information security methodology is required to address this challenge. The main objective of this work is to develop a lightweight nonlinear mechanism for digital image security using chaos theory. The proposed scheme starts by changing a plain image into an encrypted image to improve its security. A block cipher, using lightweight chaos, has been added to achieve this objective for digital image security. We utilized multiple chaotic maps to generate random keys for each channel. Also, Arnold cat map and chaotic gingerbread map are used to add confusion and diffusion. During the permutation stage, image pixels are permuted, while in diffusion stage, pixels are distorted utilizing gingerbread map to add more security. The proposed scheme has been validated using different security parameter tests such as correlation coefficient tests (CC), whose results have been observed closer to zero and information entropy (IE) value is 7.99, respectively, which is almost equal to the ideal value of 8. Moreover, number of pixels changing rate (NPCR) obtained value is higher than 99.50%, while the unified average changing intensity (UACI) is 33.33. Other parameters such as mean absolute error (MAE), mean square error (MSE), lower value of peak to signal noise ratio (PSNR), structural content (SC), maximum difference (MD), average difference (AD), normalized cross-correlation (NCC), and histogram analysis (HA) is tested. The computed values of the proposed scheme are better. The achieved results after comparison with existing schemes highlight that the proposed scheme is highly secure, lightweight, and feasible for real-time communications.

Multi-Image Encryption Algorithm Based on Cascaded Modulation Chaotic System and Block-Scrambling-Diffusion.

To address the problem of a poor security image encryption algorithm based on a single chaotic map, this paper proposes a cascade modulation chaotic system (CMCS) that can generate multiple chaotic maps. On this basis, a multi-image encryption algorithm with block-scrambling-diffusion is proposed using CMCS. The algorithm makes full use of the features of CMCS to achieve the effect of one encryption at a time for images. Firstly, the key-value associated with the plaintexts is generated using a secure hash algorithm-512 (SHA-512) operation and random sequence, and the three images are fully confused by the double scrambling mechanism. Secondly, the scrambled image is converted into a bit-level matrix, and the pixel values are evenly distributed using the bit-group diffusion. Finally, the non-sequence diffusion of hexadecimal addition and subtraction rules is used to improve the security of the encryption algorithm. Experimental results demonstrate that the encryption algorithm proposed in this paper has a good encryption effect and can resist various attacks.

Hybrid scheme for safe speech transmission based on multiple chaotic maps, watermarking and Arnold scrambling algorithm

Hybrid scheme for safe speech transmission based on multiple chaotic maps, watermarking and Arnold scrambling algorithm

A new color image encryption algorithm using multiple chaotic maps with the intersecting planes method

This paper proposes a new and efficient color image encryption algorithm based on multiple chaotic maps (Logistic map, Sine map, and Chebyshev map) and the intersecting planes method inside a cube. All maps were used to fill the three adjacent faces of the cube. These faces represent the three channels of the color image (red, green, and blue). The first phase of our method begins with extracting all the pixels from the original image and looking for the corresponding values of each pixel on the three faces of the cube. Then, we used a circular rotation operation based on the position of each pixel (row, column). This rotation prevents two identical pixels to have the same encrypted value. Afterward, we used the intersecting planes method with the corresponding face to encrypt the pixels. For the confusion part, we chose the 2D transformation Arnold Cat Map to shuffle all the pixels and change their positions according to the parameters calculated from all the pixels of the original image. We evaluated the performance of our algorithm against various differential and statistical attacks and based on multiple factors, such as histogram, entropy, correlation coefficient, UACI, NPCR, and PSNR. We compared the proposed method with several efficient methods in the field. The various results got by our method show its performance and reliability in terms of safety, precision, confidentiality, and robustness.

Image encryption based on a combination of multiple chaotic maps

Image encryption based on a combination of multiple chaotic maps

Novel Privacy Preserving Non-Invasive Sensing-Based Diagnoses of Pneumonia Disease Leveraging Deep Network Model

This article presents non-invasive sensing-based diagnoses of pneumonia disease, exploiting a deep learning model to make the technique non-invasive coupled with security preservation. Sensing and securing healthcare and medical images such as X-rays that can be used to diagnose viral diseases such as pneumonia is a challenging task for researchers. In the past few years, patients’ medical records have been shared using various wireless technologies. The wireless transmitted data are prone to attacks, resulting in the misuse of patients’ medical records. Therefore, it is important to secure medical data, which are in the form of images. The proposed work is divided into two sections: in the first section, primary data in the form of images are encrypted using the proposed technique based on chaos and convolution neural network. Furthermore, multiple chaotic maps are incorporated to create a random number generator, and the generated random sequence is used for pixel permutation and substitution. In the second part of the proposed work, a new technique for pneumonia diagnosis using deep learning, in which X-ray images are used as a dataset, is proposed. Several physiological features such as cough, fever, chest pain, flu, low energy, sweating, shaking, chills, shortness of breath, fatigue, loss of appetite, and headache and statistical features such as entropy, correlation, contrast dissimilarity, etc., are extracted from the X-ray images for the pneumonia diagnosis. Moreover, machine learning algorithms such as support vector machines, decision trees, random forests, and naive Bayes are also implemented for the proposed model and compared with the proposed CNN-based model. Furthermore, to improve the CNN-based proposed model, transfer learning and fine tuning are also incorporated. It is found that CNN performs better than other machine learning algorithms as the accuracy of the proposed work when using naive Bayes and CNN is 89% and 97%, respectively, which is also greater than the average accuracy of the existing schemes, which is 90%. Further, K-fold analysis and voting techniques are also incorporated to improve the accuracy of the proposed model. Different metrics such as entropy, correlation, contrast, and energy are used to gauge the performance of the proposed encryption technology, while precision, recall, F1 score, and support are used to evaluate the effectiveness of the proposed machine learning-based model for pneumonia diagnosis. The entropy and correlation of the proposed work are 7.999 and 0.0001, respectively, which reflects that the proposed encryption algorithm offers a higher security of the digital data. Moreover, a detailed comparison with the existing work is also made and reveals that both the proposed models work better than the existing work.

A Multiplicative-Additive Chaotic-Address Steganography

In this study, Multiple-Chaotic maps were merged by using multiplicative-additive form to generate the
 chaotic sequences which are used to track the addresses of shuffled bits in steganography. Three techniques are
 introduced for image steganography in the spatial domain. The first system is based on the well-known LSB
 technique, the second system is based on looking for the identical bits between the secret message and the cover
 image and the third system is based on the concept of LSB substitution, it is employed the mapping of secret data bits
 with cover pixel bits. It was tested and evaluated security levels for the proposed techniques by using the Peak
 Signal-to-Noise Ratio (PSNR), Mean Square Error (MSE), histogram analysis and correlative analysis and tested the
 Chaotic sequences generated by using correlation, Lypaunov exponents, Poincaré section and 0-1Test. The results
 show that the proposed methods perform better than existed systems.

Public key cryptosystem based on multiple chaotic maps for image encryption

Image is one of the most important forms of information. In this paper, two public key encryption systems are proposed to protect images from various attacks. Both systems depend on generating a chaotic matrix (<em>I</em>) using multiple chaotic maps. The parameters for these maps are taken from the shared secret keys generated from Chebyshev map using public keys for Alice and secret key for Bob or vice versa. The second system has the feature of deceiving the third party for searching for fake keys. Analysis and tests showed that the two proposed systems resist various attacks and have very large key space. The results are compared with other chaos based systems to show the superiority of these two proposed systems.