Image Encryption Framework Research Articles

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.

Image encryption framework based on multi-chaotic maps and equal pixel values quantization

Image encryption framework based on multi-chaotic maps and equal pixel values quantization

Robust multifunctional watermarking system based zero-watermarking and polar harmonic Fourier moments

The pseudo-randomness exhibited by chaotic systems has proven valuable not only in plaintext information encryption of images with varying dimensions but also as a key signal for protecting image copyright information. Image copyright protection is a crucial part of image information protection. Currently, digital watermarking technology is predominantly employed for copyright protection of digital images. However, most of the existing watermarking technologies primarily focus on the improvement of watermarking performance, neglecting the security of the watermarking system itself. Drawing inspiration from the Latin square rule, this paper proposes a novel hyperchaotic mapping modulation framework, which is employed to construct a new 3D hyperchaotic mapping and enhance the security of the fragile watermark and the zero-watermark generation process. Additionally, it is integrated into the classical image encryption framework of scrambling-diffusion to safeguard meaningful copyright information. Moreover, a functional watermarking system is designed based on the RSA asymmetric encryption algorithm, least significant bit (LSB), and polar harmonic Fourier moments (PHFMs). The proposed system enables identity verification, tampering location, and copyright authentication simultaneously. During the generation of zero watermarks, the binarization threshold of the PHFMs information matrix is determined by the method of maximum variance. The experimental results demonstrate that this scheme achieves a high level of security, better robustness to both conventional and geometric attacks, and precise localization for different types of tampering attacks.

Metaheuristic-supported image encryption framework based on binary search tree and DNA encoding

Metaheuristic-supported image encryption framework based on binary search tree and DNA encoding

Fully chaotic medical image encryption scheme based on dynamic DNA and block rotation

As an important application of image encryption, digital medical image encryption plays an important role in the field of medical health and privacy protection. This paper put forwards a fully chaotic and strongly plaintext associated image encryption framework based on an improved chaotic system, block rotation and DNA computing. The algorithm generates multiple chaotic sequences by using different one-dimensional seed chaotic maps under the sine transform framework for subsequent block rotation, DNA dynamic encoding and decoding, generating key images for DNA XOR calculation. Simulation experiments and comparative analysis have shown that this algorithm can achieve fantastic encryption performance, resist various attacks, and have higher security levels and good generalization performance.

A Modified Framework for Image Encryption and Decryption Using Modified Chaotic Algorithms Towards Medical Image Security

A Modified Framework for Image Encryption and Decryption Using Modified Chaotic Algorithms Towards Medical Image Security

Hyperchaotic Maps and the Single Neuron Model: A Novel Framework for Chaos-Based Image Encryption

With the explosion of the generation, transmission and sharing of image data over the Internet and other unsecured networks, the need for and significance of the development of novel image encryption algorithms are unprecedented. In this research work, we propose a novel framework for image encryption that is based on two hyperchaotic maps utilized in conjunction with the single neuron model (SNM). The framework entails three successive stages, where in every stage a substitution box (S-box) is applied, then XORing with an encryption key is carried out. The S-boxes and the encryption keys are generated from the numerical solutions of the hyperchaotic maps and the SNM. The performance of the proposed framework is gauged through a number of metrics, reflecting superior performance and complete asymmetry between the plain images and their encrypted versions. The main advantages of this work are (1) vast key space and (2) high encryption efficiency. The superior key space of 22551 is the result of employing the two hyperchaotic maps, while the improved efficiency, resulting in an average encryption rate of 8.54 Mbps, is the result of using the SNM as well as the employment of optimized parallel processing techniques. In addition, the proposed encryption framework is shown to output encrypted images that pass the NIST SP 800 suite. Average achieved values for the metrics include MSE of 9626, PSNR of 8.3 dB, MAE of 80.99, entropy of 7.999, NPCR of 99.6% and UACI of 31.49%.

An optimized image encryption framework with chaos theory and EMO approach

An optimized image encryption framework with chaos theory and EMO approach

3D-BCNN-Based Image Encryption With Finite Computing Precision

To resolve the finite computing precision problem in chaotic cryptography, and the security and efficiency drawbacks of deep learning-based image cryptosystems, an image encryption framework with low computing precision is presented based on three-dimensional Boolean convolution neural network (3D-BCNN). Unlike traditional CNN, the proposed 3D-BCNN is composed of only convolutional layers, in which a cross-channel 3-D Boolean convolution operation is devised without training and parameter optimization. To strengthen the security and sensitivity of the cryptosystem, the kernels and convolutional matrices are generated by the combined prime modulo multiplicative linear congruence generators, SHA-1, and a plain-image. All operations of 3D-BCNN can be conducted on devices with 8-bit word length, so the proposed encryption scheme could work well with low computing precision. Simulation results demonstrate that, with the largest computing accuracy 2−8, the proposed encryption scheme has high security and low time complexity, and can withstand various attacks.

IEFHAC: Image encryption framework based on hessenberg transform and chaotic theory for smart health.

Smart cities aim to improve the quality of life by utilizing technological advancements. One of the main areas of innovation includes the design, implementation, and management of data-intensive medical systems also known as big-data Smart Healthcare systems. Smart health systems need to be supported by highly efficient and resilient security frameworks. One of the important aspects that smart health systems need to provide, is timely access to high-resolution medical images, that form about 80% of the medical data. These images contain sensitive information about the patient and as such need to be secured completely. To prevent unauthorized access to medical images, the process of image encryption has become an imperative task for researchers all over the world. Chaos-based encryption has paved the way for the protection of sensitive data from being altered, modified, or hacked. In this paper, we present an Image Encryption Framework based on Hessenberg transform and Chaotic encryption (IEFHAC), for improving security and reducing computational time while encrypting patient data. IEFHAC uses two 1D-chaotic maps: Logistic map and Sine map for the confusion of data, while diffusion has been achieved by applying the Hessenberg household transform. The Sin and Logistic maps are used to regeneratively affect each other’s output, as such dynamically changing the key parameters. The experimental analysis demonstrates that IEFHAC shows better results like NPCR ranging from 99.66 to 100%, UACI of 37.39%, lesser computational time of 0.36 s, and is more robust to statistical attacks.

Securing e-Health application of cloud computing using hyperchaotic image encryption framework

Electronic health (e-Health) is one the latest technology that optimizes its societal benefits by implementing it in a cloud-based environment. However, the complete utilization of cloud-based e-Health facility is determined to be hampered by the challenges of security. Further, most of the data stored and transmitted as images are related to sensitive information of the patients. In this paper, a secure framework integrating e-Health and cloud using Hyper Chaos-based Image Encryption is proposed for potential implementation of security over sensitive image data transmitted over the clouds. This framework alternatively applies any one of the proposed chaos-based image encryptions and image scrambling schemes depending on the intensity and type of attack over the images shared in the cloud environment. Simulation experiments of the proposed framework are conducted to evaluate its potential in resisting known and unknown attacks that are possible under e-Health image sharing process.

Efficient Medical Image Encryption Framework against Occlusion Attack

Image encryption has attracted a lot of interest as an important security application for protecting confidential image data against unauthorized access. An adversary with the power to manipulate cipher image data can crop part of the image out to prevent decryption or render the decrypted image useless. This is known as the occlusion attack. In this paper, we address a vulnerability to the occlusion attack identified in the medical image encryption framework recently proposed in []. We propose adding a pixel scrambling phase to the framework and show through simulation that the extended framework effectively mitigates the occlusion attack while maintaining the other attractive security features. The scrambling is performed using a separate chaotic map which is securely initialized using a secret key and a random nonce to deter chosen-plaintext attacks. Moreover, we show through simulation that the choice of chaotic map used for scrambling is irrelevant to the effectiveness of the scrambling algorithm against the occlusion attack.

A novel auto-encoder induced chaos based image encryption framework aiding DNA computing sequence

The day-to-day progress in communication plays a vital role in transmitting millions and trillions of data through the unsecured network channels. It creates a way where the user’s data becomes the victim of various security threats. Among those users’ data, images act as primary data, and its encryption security methodologies are fascinating. The conventional encryption techniques don’t work well against the various other hidden security threats but require substantial computational time and cost with poor permutation performance. Hence to deal with this, an auto-encoder induced DNA (Deoxyribonucleic acid) sequence via chaotic image encryption framework is designed in our proposed work. It integrates the properties of DNA encoding and the chaotic maps to handle the data losses effectively and resist several attacks such as statistical attacks, chosen-plaintext attacks, etc. Moreover, an auto-encoder is used to control the data noises, thereby ensuring a better encryption performance. Here, the auto-encoder is activated to generate a permuted image with less time complexity and noise. A secret key is then initialized with the aid of SHA-256. Finally, image encryption and decryption are achieved, followed by the successful transmission of data over a digital network. The performance of the proposed work is analyzed with varied metrics to strengthen its efficiency over the prior techniques.

Analytic Study of a Novel Color Image Encryption Method Based on the Chaos System and Color Codes

Due to the growing of the use of Internet and communication media, image encryption is rapidly increased. Image sharing through unsafe open channels is vulnerable for attacking and stealing. For protecting the images from attacks, encryption techniques are required. Recently, new and efficient chaos-based techniques have been suggested to develop secure image encryption. This study presents a novel image encryption framework based on integrating the chaotic maps and color codes. Three phases are involved in the proposed image encryption technique. Piecewise chaotic linear map (PWLCM) is used in the first phase for permuting the digital image. In the second phase, substitution is done using Hill cipher which is the mixing of color codes with the permuted image. The third phase is implemented by XORing, a sequence generated by the chaotic logistic map (CLM). The proposed approach enhances the diffusion ability of the image encryption making the encrypted images resistant to the statistical differential attacks. The results of several analyses such as information entropy, histogram correlation of adjacent pixels, unified average changing intensity (UACI), number of pixel change rate (NPCR), and peak signal-to-noise ratio (PSNR) guarantee the security and robustness of the proposed algorithm. The measurements show that the proposed algorithm is a noble overall solution for image encryption. Thorough comparison with other image encryption algorithms is also carried out.

A chaotic framework and its application in image encryption

A novel image encryption framework is proposed in this article. A new chaotic map and a pseudorandom bit generator are proposed. Apart from this, a novel image encryption system is designed based on the proposed map and the proposed pseudorandom bit generator. These three are the major contributions of this work that makes a complete cryptosystem. The proposed new chaotic map is proposed which will be known as the ‘RCM map’ and its chaotic property is studied based on Devaney’s theory. The proposed pseudorandom bit generator is tested using the NIST test suite. The proposed method is simple to implement and does not involve any highly complex operations. Moreover, the proposed method is completely lossless, and therefore cent percent of data can be recovered from the encrypted image. The decryption process is also simple to implement i.e. just reverse of the encryption procedure. A scrambling algorithm is also proposed to further enhance the security of the overall system. The simulation, detailed analysis, and comparative studies of the proposed overall image encryption framework will help to understand the strengths and weaknesses of it. The experimental results are very promising and show the prospects of chaos theory and its usage in the field of data security.

Parallel Binary Image Cryptosystem Via Spiking Neural Networks Variants.

Due to the inefficiency of multiple binary images encryption, a parallel binary image encryption framework based on the typical variants of spiking neural networks, spiking neural P (SNP) systems is proposed in this paper. More specifically, the two basic units in the proposed image cryptosystem, the permutation unit and the diffusion unit, are designed through SNP systems with multiple channels and polarizations (SNP-MCP systems), and SNP systems with astrocyte-like control (SNP-ALC systems), respectively. Different from the serial computing of the traditional image permutation/diffusion unit, SNP-MCP-based permutation/SNP-ALC-based diffusion unit can realize parallel computing through the parallel use of rules inside the neurons. Theoretical analysis results confirm the high efficiency of the binary image proposed cryptosystem. Security analysis experiments demonstrate the security of the proposed cryptosystem.

A robust image encryption framework based on DNA computing and chaotic environment

A robust image encryption framework based on DNA computing and chaotic environment

WITHDRAWN: The generalized non-linear fresnel transform and its application to image encryption

In this research, we proposed a new image encryption method for encrypting the input color images. In this encryption process, the Fresnel transform is used for the wavelength multiplexing process. In this method, there is no need for a lens. So this technique is also called as lens-less technique. In this process, the image or photo to be encrypted will split into three channels (RGB). For decrypting this three channels, we need to use three separate encryption keys, because each channel is encrypted individually. Here the random phase masks and Fresnel transforms are acts as the decryption as well as encryption keys. In this communication, the image encryption framework according to the optical color is described. The proposed idea is simulated using the com-puter system. From the conducted simulations the working of the proposed idea has been proved.

Framework for Efficient Medical Image Encryption Using Dynamic S-Boxes and Chaotic Maps

Protecting patient privacy and medical records is a legal requirement. Traditional encryption methods fall short of handling the large volume of medical image data and their peculiar statistical properties. In this paper, we propose a generic medical image encryption framework based on a novel arrangement of two very efficient constructs, dynamic substitution boxes (S-boxes) and chaotic maps. The arrangement of S-box substitution before and after chaotic substitution is shown to successfully resist chosen plaintext and chosen ciphertext attacks. Special precautions are taken to fend off the reset attack against pseudorandom number generators. We show how to implement the generic framework using any key-dependent dynamic S-box construction method and any chaotic map. Experimental results show that the proposed framework successfully passes all security tests regardless of the chaotic map used for implementation. Based on speed analysis, we recommend the use of the classical Baker map or Henon map to achieve encryption throughput approaching 90 MB/s on a modern PC without hardware acceleration.

A Framework for Digital Image Encryption using Chaotic Baker Map with SHA Algorithm

Recently, security for the image is becoming very important. Image Encryption is the only choice in protecting the digital image by transforming the image into an unreadable format. There are many methods used to protect against unauthorized access. This research proposes a framework for image encryption using Chaos Baker map with SHA-1 algorithm. The Chaotic Baker map is a randomization technique used to make the pixels more shuffled. Key generation is essential part of image encryption, which will be carried out by SHA-1 algorithm. Experimental results show that the proposed method is well suited for high security, key sensitivity and resists various attacks.