Cipher Image Research Articles

Preserving Privacy of Classified Authentic Satellite Lane Imagery Using Proxy Re-Encryption and UAV Technologies

Privacy preservation of image data has been a top priority for many applications. The rapid growth of technology has increased the possibility of creating fake images using social media as a platform. However, many people, including researchers, rely on image data for various purposes. In rural areas, lane images have a high level of importance, as this data can be used for analyzing various lane conditions. However, this data is also being forged. To overcome this and to improve the privacy of lane image data, a real-time solution is proposed in this work. The proposed methodology assumes lane images as input, which are further classified as fake or bona fide images with the help of Error Level Analysis (ELA) and artificial neural network (ANN) algorithms. The U-Net model ensures lane detection for bona fide lane images, which helps in the easy identification of lanes in rural areas. The final images obtained are secured by using the proxy re-encryption technique which uses RSA and ECC algorithms. This helps in ensuring the privacy of lane images. The cipher images are maintained using fog computing and processed with integrity. The proposed methodology is necessary for protecting genuine satellite lane images in rural areas, which are further used by forecasters, and researchers for making interpretations and predictions on data.

Encipher GAN: An End-to-End Color Image Encryption System Using a Deep Generative Model

Chaos-based image encryption schemes are applied widely for their cryptographic properties. However, chaos and cryptographic relations remain a challenge. The chaotic systems are defined on the set of real numbers and then normalized to a small group of integers in the range 0–255, which affects the security of such cryptosystems. This paper proposes an image encryption system developed using deep learning to realize the secure and efficient transmission of medical images over an insecure network. The non-linearity introduced with deep learning makes the encryption system secure against plaintext attacks. Another limiting factor for applying deep learning in this area is the quality of the recovered image. The application of an appropriate loss function further improves the quality of the recovered image. The loss function employs the structure similarity index metric (SSIM) to train the encryption/decryption network to achieve the desired output. This loss function helped to generate cipher images similar to the target cipher images and recovered images similar to the originals concerning structure, luminance and contrast. The images recovered through the proposed decryption scheme were high-quality, which was further justified by their PSNR values. Security analysis and its results explain that the proposed model provides security against statistical and differential attacks. Comparative analysis justified the robustness of the proposed encryption system.

Towards Deep Learning for Efficient Image Encryption

Deep learning has become a topic of great concern in many domains and also in end-to-end image encryption. Traditional image encryption techniques employ rounds of diffusion and confusion to obtain an explicit trade-off between security and efficiency. With deep learning approach adequate solutions are obtained for current challenges in image encryption schemes such as efficiency, cryptographic strength, etc. In this paper, the latest trends in end-to-end encryption schemes based on deep learning are summarized. Firstly, the existing deep learning-based encryption systems are categorized into three categories: encryption with style transfer, Style transfer with enhanced diffusion properties, and Combining Deep Neural networks with chaotic systems. Each of these methodologies is discussed and relevant conclusions are made. Secondly, these methods are compared and analyzed for their achievements and drawbacks in terms of cryptographic properties of generated cipher images and quality of the recovered images. Third, the possibility of new cryptographic attacks are discussed such as Hidden Factors Leakage and Network Architecture Leakage as consequences of combination of deep learning approaches with an encryption system. Finally, conclusions are made based on comparison and analysis of deep learning approach in end-to-end encryption/decryption systems, providing basis for further research.

Performance of Cipher Image Transmission in Free Space Optics Under Foggy Weather

Performance of Cipher Image Transmission in Free Space Optics Under Foggy Weather

An Improved Analysis of Secured Permutation and Substitution based Image Encryption

The transmission and storage of digital data raises serious security concerns as information technology evolves at a breakneck pace. To ensure the safety of the transferred data, security methods must be put in place. Encrypting an image is a method of protecting sensitive data by converting it into an unrecognizable format. The procedure includes access control, privacy, validation, and copyright protection. Cryptography, steganography, and watermarking are three distinct methods to prevent unauthorized access to digital data. Of these three methods, cryptography has emerged as one of the most important ways to ensure complete safety. Therefore, a secure and efficient cipher algorithm is required for trustworthy communication. In this work, we offer a practical Secured Asymmetric Image Cipher (SAIC) Algorithm for encrypting images with a secret key of arbitrary length. At first, the KG algorithm creates two unique keys. Both the encryption and decryption processes require a key. The experimental results reveal that the encrypted image lacks the original image's independence (NPCR 99.89%, UACI 36.89%). The suggested approach has a high encryption rate, can be implemented easily, and is computationally secure. The reproduced data validates the safety and practicability of the proposed architecture.

Fast Image Encryption Algorithm for Logistics-Sine-Cosine Mapping.

Because images are vulnerable to external attacks in the process of network transmission and traditional image encryption algorithms have limitations such as long encryption time, insufficient entropy or poor diffusion of cipher image information when encrypting color images, a fast image encryption algorithm based on logistics-sine-cosine mapping is proposed. The algorithm first generates five sets of encrypted sequences from the logistics-sine-cosine mapping, then uses the order of the encryption sequence to scramble the image pixels and designs a new pixel diffusion network to further improve the key sensitivity and plain-image sensitivity of the encryption algorithm. Finally, in a series of security analysis experiments, the experimental image Lena was tested 100 times, and the average encryption time was 0.479 s. The average value of the information entropy, pixel change rate and uniform average change intensity of the cipher image reached 7.9994, 99.62% and 33.48%, respectively. The experimental results show that the fast image encryption algorithm based on logistics-sine-cosine mapping takes less time to encrypt, and the cipher image has good information entropy and diffusivity. It is a safe and effective fast image encryption algorithm.

A Novel Image Encryption Algorithm Based on 2D Self-Coupling Sine Map

Since the current general image encryption algorithms based on chaotic systems and classical permutation diffusion structures typically suffer from stochastic degradation, simple algorithm structures, and reduced execution efficiency when dealing with images with large data, this paper proposes a new multiprocess image encryption algorithm based on a new hyperchaotic coupled sine map. To begin with, by performing dynamic analyses of the map, including equilibrium points, bifurcation diagrams, Lyapunov exponent diagrams, and phase diagrams, associated performance analyses show high spectral entropy (SE) values and permutation entropy (PE) values. Meanwhile, sequences generated by the proposed map are able to pass the NIST randomness test, which shows its suitability for the design of encryption algorithms. Then, a chaos-based image encryption algorithm is proposed in this paper: In the preprocessing stage, the images are sparsely sampled using a compressed-sensing method; in the permutation stage, the image is decomposed into eight bit-plane images containing only 0s and 1s and a clockwise cyclic shift operation based on a chaotic sequence is applied in each bit-plane image to achieve a bit-level permutation effect; in the diffusion stage, the block DNA random computing is applied to the permuted image to modify the pixel values and the final cipher image is obtained. In addition, the initial value of the map is determined by the preset initial value, user-defined strings, and the Hash value of the plain image, where the Hash value of the plain image is calculated by the SHA-256 algorithm. Finally, the analysis of the algorithm demonstrates that the algorithm exhibits extremely strong plaintext correlation externally and excellent encryption performance in terms of attack resistance and execution efficiency.

DHAN: Encrypted JPEG image retrieval via DCT histograms-based attention networks

In image retrieval, the images to be retrieved are stored on remote servers. Since the images contain amounts of privacy information and the server cannot be fully trusted, people usually encrypt their images before uploading them to the server, which raises the demand for encrypted image retrieval (EIR). Current EIR techniques extract ruled hand-craft features from cipher images first and then build retrieval models (e.g., support vector machine, SVM) by these features, or use deep learning models (e.g., Convolutional Neural Network, CNN) to learn cipher-image representations in an end-to-end manner. However, SVM is not skilled at learning non-linear embedding in complex image database, and end-to-end EIR leads to low image security or retrieval performance because CNN is sensitive to extreme chaotic cipher images. Not-end-to-end EIR offers excellent encryption performance, and deep learning can further mine non-linear embedding from ruled hand-craft features. To this end, we propose a novel EIR scheme, named discrete cosine transform (DCT) Histograms-based Attention Networks (DHAN), which is based on deep learning to enhance expression ability of cipher-image in a not-end-to-end manner. Specifically, the DCT coefficients of images are encrypted by value replacement and block permutation encryption, and then the effective histogram features of DCT coefficients are extracted from the cipher images since the sets of DCT frequency in encrypted images are similar to that of plain images. After that, to dynamically learn the salient features of cipher images, we propose a new module named ResAttention and design deep attention networks to provide retrieval. Extensive experiments on two datasets demonstrate that DHAN not only provides high image security but also greatly improves retrieval performance than that of existing schemes.

Optimized Deep Learning-Enabled Hybrid Logistic Piece-Wise Chaotic Map for Secured Medical Data Storage System

Background: In recent times, medical technology has generated massive reports such as scanned medical images and electronic patient accounts. These reports are necessary to be stored in the highly secured platform for further reference. Traditional storage systems are infeasible for storing massive data. In addition, it suffers to provide secure storage and privacy protection at the time of medical services. It is necessary to provide secure storage and full utilization of personal medical records for the common people in practice. The healthcare system based on IoT enhances the support for the patients and doctors in diagnosing the sufferers at an accurate time using the monitored health data. Yet, doctors make an inappropriate decision regarding the sufferer’s sickness when the information regarding health data saved in the cloud gets lost or hacked owing to an external attack or also power failure. Hence, it is highly essential for verifying the truthfulness of the sufferer’s information regarding health data saved on the cloud.Hypothesis: The major intention of this task is to adopt a new chaotic-based healthcare medical data storage system for storing medical data (medical images) with high protection. Methodology: Initially, the input medical images are gathered from the benchmark datasets concerning different modalities. The collected medical images are enciphered by developing Hybrid Chaotic Map by adapting the 2D-Logistic Chaotic Map (2DLCM), and Piece-Wise Linear Chaotic Map (PWLCM) referred to as Hybrid Logistic Piece-Wise Chaotic Map (HLPWCM). An Optimized Recurrent Neural Network (O-RNN) is proposed for key generation using Best Fitness-based Coefficient vector improved Spotted Hyena Optimizer (BF-CSHO). The O-RNN-based key generation utilizes the extracted image features like first and second-order statistical features and the targets are acquired as a unique encrypted key, which is used for securing the medical data. The same BF-CSHO is used for improving the training algorithm (weight optimization) of RNN to minimize the Mean Absolute Error (MAE) between the cipher (encrypted) images and original images. Results: From the result analysis, the suggested BF-CSHO-RNN-HLPWCM, by considering the image size at [Formula: see text] shows 10.4%, 8.5%, 3.97%, 0.62%, 3.88%, 2.40%, and 7.82% provides better computational efficiency than LCM, PWLCM, LPWCM, PSO-RNN-HLPWCM, JA-RNN-HLPWCM, GWO-RNN-HLPWCM, and SHO-RNN-HLPWCM, respectively. Conclusion: Thus, the simulation findings show the effective efficiency of the offered method owing to the security of the stored medical data.

Mayfly optimization with chaos-based image encryption technique for cloud computing environment

Cloud computing (CC) technologies become popular due to their characteristics of maximum availability, flexibility, and scalability. Despite the benefits of CC, security becomes a major challenging issue that can be resolved by the use of image encryption techniques. Numerous works have been carried out with several approaches, and effective models need to be designed to enhance secure image encryption algorithms. Recently, chaotic approaches have been available in various domains, such as design of cryptosystems and image encryption. Chaos-based methods utilize random chaos sequences for image encryption, which are highly secure and fast. In this view, this study introduces a unique mayfly optimization with chaos-based image encryption (MFO-CBIE) technique for CC environment. The major intention of the MFO-CBIE model is to accomplish security in the CC environment via encryption and key generation process. The proposed MFO-CBIE model follows a three-stage process, namely image encryption, optimal key generation, and image decryption. In addition, the MFO-CBIE model employs the MFO algorithm for the optimal key generation process of the chaos-based encryption technique. Once the images are encrypted, they are securely transmitted to the cloud server. On the receiver side, the cipher image gets accessed from the cloud server, and then the decryption process is carried out. The experimental evaluation of the MFO-CBIE model is tested using benchmark images, and the results ensured the promising performance of the MFO-CBIE model over recent approaches.

Plaintext-related image encryption scheme without additional plaintext based on 2DCS

In the latest research on image compression and encryption, more and more encryption schemes associate key generation mechanism with plaintext to resist various attacks. This paper presents an image encryption scheme related to plaintext based on 2D compressive sensing(2DCS). It is a plaintext correlation compression and encryption scheme with no additional information. The chaotic sequence generated by Henon map randomly selects some positions in the plaintext image. Double random-phase encoding(DRPE) is applied to the pixels to establish the relationship between the plaintext and the key. The encryption process is divided into three parts. The first part is to map out these key pixels and perform double negative positive transformation(DNPT). In the second part, the plaintext image is sampled, quantified and reconstructed by 2DCS. DNA calculation and diffusion were performed on the reconstructed vector. In the third part, the key pixel matrix and diffusion matrix of DNPT are inversely reconstructed to obtain the cipher image. The proposed encryption mechanism can avoid the transmission of additional information related to the plaintext in the channel, which greatly saves the transmission cost and key storage space. The effectiveness and feasibility of the scheme are verified by various security analyses.

IWT and RSA based asymmetric image encryption algorithm

An asymmetric image encryption algorithm based on integer wavelet transformation (IWT) and Rivest-Shamir-Adleman (RSA) algorithm is proposed. Firstly, two plain characteristic parameters (PCP) of the plain image are extracted and two random numbers are chosen. Then, a new parameter transformation model (PTM) is constructed to do nonlinear processing for them, and three cipher characteristic parameters (CCP) are got. After applying RSA operation for CCP (seen as plain messages), three cipher messages are obtained. Secondly, a new initial value obtaining model (IOM) for all plain messages and cipher messages is established, by which initial values of 3D chaotic system are produced. Then, three chaotic sequences can be generated. Thirdly, chaotic sequences are used to confuse the plain image by a way of row-column cycle. Then, IWT operation is carried out and the above chaotic sequences are employed to confuse again the wavelet coefficients. Thereafter, inverse IWT is applied to get the confused image, realizing double confusion operations on both spatial domain and frequency domain. Finally, the confused image is diffused as a whole to get the cipher image. Experiment results explain that the proposed algorithm can realize the encryption in short time, and resist effectively against brute-force attack and noise attack.

An Effective Approach to Scramble Multiple Diagnostic Imageries Using Chaos-Based Cryptography

Medical image encryption could aid in preserving patient privacy. In this article, we provide a chaotic system-based medical picture encryption method. The diffusion and permutation architecture was used. The permutation based on plain image and chaotic keys is offered to shuffle the plain picture's pixels to other rows and columns, successfully weakening the strong connections between neighboring pixels. Diffusion is suggested to spread small changes of plain images to all of the pixels in cipher images to enhance the encryption effect. We analyze the chaotic behavior of the proposed system using various techniques and tests such as bifurcation plots, Lyapunov exponents, MSE, PSNR tests, and histogram analysis.

Lossless embedding: A visually meaningful image encryption algorithm based on hyperchaos and compressive sensing

A novel visually meaningful image encryption algorithm is proposed based on a hyperchaotic system and compressive sensing (CS), which aims to improve the visual security of steganographic image and decrypted quality. First, a dynamic spiral block scrambling is designed to encrypt the sparse matrix generated by performing discrete wavelet transform (DWT) on the plain image. Then, the encrypted image is compressed and quantified to obtain the noise-like cipher image. Then the cipher image is embedded into the alpha channel of the carrier image in portable network graphics (PNG) format to generate the visually meaningful steganographic image. In our scheme, the hyperchaotic Lorenz system controlled by the hash value of plain image is utilized to construct the scrambling matrix, the measurement matrix and the embedding matrix to achieve higher security. In addition, compared with other existing encryption algorithms, the proposed PNG-based embedding method can blindly extract the cipher image, thus effectively reducing the transmission cost and storage space. Finally, the experimental results indicate that the proposed encryption algorithm has very high visual security.

A one-round medical image encryption algorithm based on a combined chaotic key generator.

Medical images of patients must be securely transmitted and kept private in telemedicine. To secure such medical images, this paper proposes a single round chaotic image encryption scheme based on a permutation-diffusion structure. A combined chaotic key generator (CCKG) is proposed to enhance key sensitivity and generation in order to improve the security of medical images to be encrypted. CCKG is used to produce the initial seeds for the fractional order chaotic system (FOCS) and Lorenz system (LS) for the permutation and diffusion processes, respectively. CCKG together with proposed permutation and diffusion methods enhances cipher image security in single round. Using zigzag transform (ZT) scanning, the plain image is first permuted block by block. The type of scanning used on each block is heavily influenced by the ZT selection from FOCS and LS. Following block-wise permutation, the permutation order (PO) generated from LS performs overall permutation. Based on the pixel positions, the overall permuted image is divided into odd and even parts. Then these parts of the images are diffused separately by the random pixel matrices generated by LS and FOCS. The cipher image is formed by combining the odd and even parts after diffusion. Secret key analysis, statistical analysis, differential attack analysis, and simulations confirm that with a single round of image encryption, the proposed system is competent, robust and effective.

Semiconductor superlattice physical unclonable function based two-dimensional compressive sensing cryptosystem and its application to image encryption

Most image encryption methods are symmetric cryptosystems, in which the encryptor and decryptor are required to share the same key. To reduce the risk of key generation and key distribution for image cipher scheme, in this paper, we present a novel scheme based on semiconductor superlattice physical unclonable function (SSL-PUF) and two-dimensional compressive sensing (2DCS). Distinguished from other existing CS-based cryptosystems, SSL-PUF with quantum tunneling induced chaos is utilized, in which the key distribution for the public channel can be achieved by a pair of matched SSL-PUF. First, the seed key generated from SSL-PUF is extended with the pseudo-random coupled map lattices (PRCML) module. Next, gradient-type alternating minimization approach is employed to solve the low coherence measurement matrix. Finally, compressing and encrypting can be implemented simultaneously by measuring the plain image bi-directionally. Moreover, the total variation (TV) regularization reconstruction method is optimized based on the Adam algorithm. With low compression ratios and computationally constrained environments, the proposed optimization method can reconstruct images with high quality. Numerous experiments were simulated and analyzed to validate that the proposed 2DCS-based cryptosystems possess well performance, such as image reconstruction effect, security against various attacks, and good robustness.

A novel image encryption mechanism based on umbrella map and Yang-Gu algorithm

A novel image encryption algorithm based on nonlinear discrete chaotic umbrella map and Yang-Gu mixture amplitude-phase retrieval algorithm in fractional Fourier domain is proposed in this paper. Here in the proposed mechanism the input image is encrypted with a deterministic phase mask and later subjected to fractional Fourier transform. Thereafter, for scrambling the pixels of transformed image the umbrella map is utilized. The umbrella map along with providing a broader range of control parameters also enhances the key space. Scrambled image is further encoded with additional deterministic phase mask before subjecting it to another level of fractional Fourier transform. The phase truncated portion of the resultant image is used as an input to the Yang-Gu mixture amplitude-phase retrieval algorithm in the Fourier domain. The encrypted image attained by one-way binary modulation is real-valued, which makes it more appropriate for storage and transmission. Comprehensive statistical and attack analysis of the proposed scheme has been carried out. Sensitivity analysis of the scheme to various keys and crucial parameters has also been done. The results demonstrate the robustness and efficacy of the proposed mechanism. The exceptional resistance to noise attacks makes it suitable for use in extremely noisy channels and real valued cipher image is more convenient to store and transmit.

A novel meaningful image encryption algorithm based on newly-designed coupled map lattice and adaptive embedding

In this paper, a novel visually meaningful encryption algorithm is proposed by combining ILCM-Dynamic Coupled Map Lattice (IDCML), compressive sensing (CS), and adaptive embedding. First, the 2D discrete wavelet transform (DWT) is adopted to sparse the plain image. Then the sparse matrix after threshold processing is scrambled by Fisher-Yates confusion, and the key-controlled partial Hadamard matrix is used for measurement. Finally, the gray level co-occurrence matrix (GLCM) is used to analyze the texture complexity of the carrier image for adaptive embedding, and the final visually meaningful cipher image is obtained. Compared with traditional encryption algorithms that generate noise-like cipher images, visual image encryption achieves the dual protection of digital images in content and vision, and adaptive embedding also avoids the problem that improper embedding location will affect the quality of encryption and decryption. To achieve higher security, a new combined ICMIC and Logistic chaotic map (ILCM) is first presented, then a novel ILCM dynamic coupling mapping lattice with ILCM as dynamic coupling coefficient is proposed and used in the process of compression, encryption and embedding. The experimental results and comprehensive analyses demonstrate that the proposed encryption algorithm has superior visual security and decryption quality.

Binary decomposition-based image cipher algorithm with flexible method for key construction

<span>Image security is still one of the important fields in multimedia processing because it’s used in our daily lives. Watermarking, steganography, and ciphering are three directions to keep an image secret. Encryption defined as the process of changing information (which called plaintext) into an unreadable secret format (which called cipher-text). A new ciphering algorithm based on binary decomposition and binary codes conversion is proposed in this paper. The key is constructed in a flexible way based on the size of a secret image using some logical operations to increase the security levels. Three test images in different sizes were used to evaluate the performance of the proposed algorithm. The results of the visual scene and statistical factors proved that the suggested method was ciphering the image with high security. The proposed work was validated to confirm its effectiveness. As conclusions, the uses of decomposition and simple binary operations have given high-level image security. Also, key construction is an important step to face several types of attackers.</span>

A novel pixel-split image encryption scheme based on 2D Salomon map

Images serve as a valuable information carrier for their properties of visualization and holding a large amount of messages. This paper presents a novel chaos-based encryption scheme to protect images from being captured or attacked. Firstly a two-dimensional Salomon map inspired by the Salomon function is proposed and there is a comprehensive comparison with other recent counterparts to validate the effectiveness of this chaotic system. Compared with traditional maps, the results show that 2D Salomon map has larger Lyapunov exponent, Kolmogorov entropy, Correlation dimension and Sample entropy. Especially the values of Lyapunov exponent can reach 11, which indicates that the chaotic sequence generated by Salomon map has strong randomness. Then a pixel-split image encryption scheme supported by 2D Salomom map is presented, itselectively exchanges the high and low bits of the image and globally spreads the altered pixels to random positions, resulting in that the cipher image contains no useful information any more and can only be recovered with a unique key. Ultimately, we have conducted comprehensive tests on the proposed algorithm, including various simulated attack, randomness tests and decorrelation analysis on cipher images, the overall results show that the scheme has excellent cryptographic effect and security.