Image Encryption Research Articles

Image Retrievable Encryption Based on Linear Fitting and Orthogonal Transformation

Abstract With the development of cloud computing, an increasing number of resource-constrained image owners tend to store their images in the cloud and rely on image retrieval services to obtain the images they desire. However, the security of the cloud cannot be fully guaranteed. To ensure image security while achieving good retrieval performance, we have designed a retrievable image encryption algorithm based on linear fitting and orthogonal transformation. This algorithm first generates encryption and feature extraction domains through orthogonal decomposition, and then applies a modified ResNet50 network for feature extraction in the feature extraction domain. The encryption process employs an improved affine transformation based on linear fitting, where part of the fitting values comes from the original image data and the other part comes from data generated by a chaotic system. Additionally, to simplify the measurement of feature similarity in the cloud, we have designed a hierarchical feature index tree to narrow the retrieval scope, thereby reducing retrieval complexity. Experimental results show that the proposed algorithm effectively protects image privacy and achieves high retrieval accuracy. The F-score reached 6.7634% on the Ghim10k dataset and 25.514% on the Corel 1K dataset, significantly improving upon traditional methods. This algorithm has potential application value in the fields of secure image storage and efficient retrieval in the cloud.

Group information enhances recognition of both learned and unlearned face appearances.

Are people better at recognizing individuals of more relevant groups, such as ingroup compared to outgroup members or high-status compared to low-status individuals? Previous studies that associated faces with group information found a robust effect of group on face recognition but only tested it using the same images presented during the learning phase. They therefore cannot tell whether group information enhances encoding of the specific image presented during learning or encoding of the person who appears in it, which should generalize to other images of that person. In addition, the measures used in these studies do not sufficiently distinguish between sensitivity and response bias. In this article, we addressed these limitations and examined in three experiments the effect of group membership (Experiments 1 and 2) and social status (Experiment 3) on face recognition. In all experiments, we assessed recognition of both learned and unlearned views of the learned faces. Our results show improved recognition of ingroup members compared to outgroup members and of individuals of high-status groups compared to low-status groups for both learned and unlearned views. These effects emerged also when we used measures of memory accuracy that adequately control for response bias. These findings highlight the importance of group and status information in person recognition. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Retraction Note: Cyber security analysis based medical image encryption in cloud IoT network using quantum deep learning model

Retraction Note: Cyber security analysis based medical image encryption in cloud IoT network using quantum deep learning model

The implementation of algebraic complex lookup tables over non-chain Galois ring extensions and Henon map in multimedia security

Abstract Image encryption is crucial for web-based data storage and transmission. Complex algebraic structures play a vital role in providing unique features and binary operations. However, current algebraic-based techniques face challenges due to limited key space. To tackle this issue, our study uniquely connects the algebraic structures with a chaotic map. The study introduces a complex non-chain Galois ring structure and a 12-bit substitution box for image substitution. An affine map is utilized to permute image pixels, and the 12-bit substitution box is uniquely mapped to a Galois field for encryption. A two-dimensional Henon map is employed to generate different keys for the XOR operation, resulting in an encrypted image. The resilience of the scheme against various attacks is evaluated using statistical, differential, and quality measures, showcasing its effectiveness against well-known attacks.

A Hybrid Approach of Substitution and Permutation techniques for Modern Image-Cryptosystem

Abstract This paper proposes a new image encryption algorithm based on the introduction of cellular automata with Galois field-based methods to further encourage a higher level of security. This begins the encryption process by creating an initial substitution box from the irreducible polynomials in the Galois field, followed by the final substitution box obtained through permutation operations. This lays
the foundation for a sound cryptographic process. In order to handle different cryptographic attack types, balance in the substitution box has been closely analyzed. Additionally, we leverage the chaotic nature of the logistic map to incorporate a random element, significantly strengthening the encryption scheme against diverse cryptanalytic attacks. Nonlinear dynamics, as introduced by cellular automata, further develop the encryption process and disperse the image’s data, making it more intricate for any potential attacker. The differential analysis validates the encryption scheme’s resistance to differential attacks, thereby confirming its potential for secure image transmission and storage. The proposed hybrid approach presents a comprehensive solution for image encryption, ensuring high security and computational efficiency. This contribution is an added advantage in the development of image cryptosystems,
addressing the modern challenges facing secure data within the digital landscape.

Multi-Model Style-Aware Diffusion Learning for Semantic Image Synthesis

Semantic image synthesis aims to generate images from given semantic layouts, which is a challenging task that requires training models to capture the relationship between layouts and images. Previous works are usually based on Generative Adversarial Networks (GAN) or autoregressive (AR) models. However, the GAN model's training process is unstable, and the AR model’s performance is seriously affected by the independent image encoder and the unidirectional generation bias. Due to the above limitations, these methods tend to synthesize unrealistic, poorly aligned images and only consider single-style image generation. In this paper, we propose a Multi-model Style-aware Diffusion Learning (MSDL) framework for semantic image synthesis, including a training module and a sampling module. In the training module, a layout-to-image model is introduced to transfer the learned knowledge from a model pretrained with massive weak correlated text-image pairs data, making the training process more efficient. In the sampling module, we designed a map-guidance technique and creatively designed a multi-model style-guidance strategy for creating images in multiple styles, e.g., oil painting, Disney Cartoon, and pixel style. We evaluate our method on Cityscapes, ADE20K, and COCO-Stuff, making visual comparisons and computing with multiple metrics such as FID, LPIPS, etc. Experimental results demonstrate that our model is highly competitive, especially in terms of fidelity and diversity.

Image–Text Matching Model Based on CLIP Bimodal Encoding

Image–text matching is a fundamental task in the multimodal research field, connecting computer vision and natural language processing by aligning visual content with corresponding textual descriptions. Accurate matching is critical for applications such as image captioning and text-based image retrieval yet remains challenging due to the differences in data modalities. This paper addresses these challenges by proposing a robust image–text matching model inspired by Contrastive Language–Image Pre-training (CLIP). Our approach employs the Vision Transformer (ViT) model as the image encoder and Bidirectional Encoder Representations from Transformers (Bert) as the text encoder, integrating these into a shared vector space to measure semantic similarity. We enhance the model’s training efficiency using the LiT-tuning paradigm to optimize learning through a cosine decay strategy for dynamic adjustment of the learning rate. We validate our method on two benchmark datasets, WuKong and Flickr30k, demonstrating that our model achieves superior performance and significantly improves key evaluation metrics. The results underscore the model’s effectiveness in achieving accurate and robust image–text alignment.

Robust double encryption and watermarking algorithms for color watermark images

Abstract Current digital watermarking technologies mainly focus on the imperceptibility and robustness of watermark embedding, while the security of watermarking images is also worth further research. Considering nonlinear characteristics and the integration structure of storage and computation, memristors can be introduced into encryption algorithms to improve the effect of encryption. The paper proposes a double encryption algorithm for color watermark images based on MCNN (Memristive Cellular Neural Networks) and Arnold transform, generates chaotic sequences for watermark image encryption by introducing memristors to the CNN (Cellular Neural Networks) to construct MCNN, scrambles the images using the Arnold transform to achieve the double encryption of pixel values and pixel positions, and enhances the security of the watermark images. Adopting the SE (Spectral Entropy) complexity algorithm optimizes the parameters of MCNN, and improves the performance of the double encryption algorithm. The embedding and extraction of the encrypted watermark image is realized by the algorithm combining CT (Contourlet Transform) and SVD (Singular Value Decomposition), which enhances the ability to resist common attacks such as compression and rotation attacks. Experiment results show the proposed algorithms can better maintain the quality of the color watermark images, break the statistical characteristics of the original images, and the generated key has good randomness. In addition, the presented algorithms are highly sensitive to the key, and improve the ability to resist statistical attacks, differential attacks, exhaustive attacks and common image attacks with good security, robustness and imperceptibility.

Ultra-security optical image encryption using speckles through a multi-mode fiber

To realize optical image encryption for long-distance transmission while considering its security performance, an optical image encryption scheme is proposed in this paper. In the scheme, the pixel information of the plaintext image is first encoded by orbital angular momentum (OAM) holograms; then, the information-coded OAM beam is transmitted through a 1 km multimode fiber to generate speckles as ciphertexts for encryption; and finally, a pre-trained deep learning model capable of learning the relationship between the output speckles and the input information-coded OAM modes is used for decryption. The proposed scheme is not only able to achieve high fidelity recovered image but also a remarkable level of security. The high security stems from the combined use of three keys: the order key, the model key, and the coder key, during optical encryption. The image can therefore only be decrypted by authorized users who simultaneously know the three keys. We have experimentally demonstrated the high fidelity and high security encryption/decryption capabilities. Our work can provide a promising avenue for further research into long-distance optical image transmission and encryption with ultra-high security.

Privacy-Preserving ConvMixer Without Any Accuracy Degradation Using Compressible Encrypted Images

We propose an enhanced privacy-preserving method for image classification using ConvMixer, which is an extremely simple model that is similar in spirit to the Vision Transformer (ViT). Most privacy-preserving methods using encrypted images cause the performance of models to degrade due to the influence of encryption, but a state-of-the-art method was demonstrated to have the same classification accuracy as that of models without any encryption under the use of ViT. However, the method, in which a common secret key is assigned to each patch, is not robust enough against ciphertext-only attacks (COAs) including jigsaw puzzle solver attacks if compressible encrypted images are used. In addition, ConvMixer is less robust than ViT because there is no position embedding. To overcome this issue, we propose a novel block-wise encryption method that allows us to assign an independent key to each patch to enhance robustness against attacks. In experiments, the effectiveness of the method is verified in terms of image classification accuracy and robustness, and it is compared with conventional privacy-preserving methods using image encryption.

Evaluating ASCON Lightweight Encryption Algorithm for Image Encryption

Evaluating ASCON Lightweight Encryption Algorithm for Image Encryption

Epsilon negative dynamic coded THz metamaterial beamformer for digital information encryption

Light propagation facilitates digital information encryption by utilizing Epsilon Negative (ENG) metamaterials as a medium. Achieving the desired encryption hinges on synchronizing two pivotal features: the phase difference and the epsilon shifting of the metamaterials. The proposed metamaterial is intricately designed to represent digital bit 1 (states 0 and 1), contingent upon the arrangement of material multilayers within the metamaterial device. Specifically, state 1 is fashioned through a combination of Graphene and Gold, while state 0 is solely represented by Graphene on a metamaterial structure. The developed structure exhibits right hand metamaterial (epsilon of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:+$$\\end{document}7.20) for state 0 and plasma metamaterial (epsilon \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:-$$\\end{document}6.94) for state 1 at 8.6 THz. Notably, the phase difference between 0 and 1 states is around \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:90^\\circ\\:$$\\end{document}. Primarily leveraging the synchronized combination phase and epsilon as the key design parameters for the metamaterial array, the beamforming pattern enables beam steering within the angular range of \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:+30^\\circ\\:$$\\end{document} to \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\:-30^\\circ\\:$$\\end{document}. Secondly, employing a sophisticated material addition strategy involving Gallium Arsenide (GaAs) systematically manipulates the sidelobe in specific directions, thereby providing a mechanism to modulate the sidelobe and concurrently augment spatial resolution. After preparing the metamaterial coded (MC) lens, the source, lens and receiver strategically encrypt the scanning image, employing techniques including Doppler compression and 2D discrete Fourier transform. The proposed MC lens offers applicability like image encryption, security scanning, conveyor systems, hyperlenses and modern cryptography.

Constructing a novel n-dimensional chaotic map with application to image encryption

Constructing a novel <i>n</i>-dimensional chaotic map with application to image encryption

Novel chaotic image cryptosystem based on dynamic RNA and DNA computing

In view of the security problems of image encryption algorithms encoded by single DNA or RNA, to increase the randomness of the diffusion process and the uncertainty of the coding rules, we propose a combining dynamic RNA and DNA computing based chaotic image encryption algorithm, which has a more complicated encryption process for improving the security of the encryption algorithm and increases the difficulty of decoding. First, a new three-dimensional hyperchaotic map is proposed, which exhibits a rich set of dynamic behaviors. Second, the sequences generated by the proposed map are passed to NIST test with good randomness and implemented by digital signal processing hardware, which shows the feasibility of the proposed chaotic map for industrial applications. Second, the K-means algorithm is used to split the plaintext into two parts. Third, the chaotic sequence is used to displace and diffuse the two parts of the plaintext, respectively. Then, chaotic sequences were used to encode using dynamic DNA and RNA of these two parts, respectively. Then, the chaotic sequences were used to compute the dynamic DNA and RNA computing of these two parts, respectively. Finally, the cipher text is decoded accordingly. The experimental results show that compared with some related encryption algorithms, our method has higher security.

SAMCT: Segment Any CT Allowing Labor-Free Task-Indicator Prompts.

Segment anything model (SAM), a foundation model with superior versatility and generalization across diverse segmentation tasks, has attracted widespread attention in medical imaging. However, it has been proved that SAM would encounter severe performance degradation due to the lack of medical knowledge in training and local feature encoding. Though several SAM-based models have been proposed for tuning SAM in medical imaging, they still suffer from insufficient feature extraction and highly rely on high-quality prompts. In this paper, we propose a powerful foundation model SAMCT allowing labor-free prompts and train it on a collected large CT dataset consisting of 1.1M CT images and 5M masks from public datasets. Specifically, based on SAM, SAMCT is further equipped with a U-shaped CNN image encoder, a cross-branch interaction module, and a task-indicator prompt encoder. The U-shaped CNN image encoder works in parallel with the ViT image encoder in SAM to supplement local features. Cross-branch interaction enhances the feature expression capability of the CNN image encoder and the ViT image encoder by exchanging global perception and local features from one to the other. The task-indicator prompt encoder is a plug-and-play component to effortlessly encode task-related indicators into prompt embeddings. In this way, SAMCT can work in an automatic manner in addition to the semi-automatic interactive strategy in SAM. Extensive experiments demonstrate the superiority of SAMCT against the state-of-the-art task-specific and SAM-based medical foundation models on various tasks. The code, data, and model checkpoints are available at https://github.com/xianlin7/SAMCT.

A plaintext-related and ciphertext feedback mechanism for medical image encryption based on a new one-dimensional chaotic system

Abstract In recent years, Plaintext-Related Image Encryption (PRIE) algorithms have been introduced, demonstrating a commendable level of plaintext sensitivity to resist chosen plaintext attack (CPA). However, these approaches suffer from several drawbacks, including inability to fully reconstruct the original image, limited practical value and excessive computational demands etc.. Moreover, the exponential expansion of medical data necessitates the formulation of more secure and efficient encryption algorithms. In this paper, firstly, a novel one-dimensional chaotic map, designated as 1D-SAM, which strikes an excellent balance between structural complexity and chaotic performance is proposed. The 1D-SAM achieve a larger chaotic range and an elevated Lyapunov exponent, signifying enhanced dynamical complexity. Subsequently, we devise a lightweight medical image encryption system leveraging the 1D-SAM and an innovative diffusion architecture, termed the plaintext-related and ciphertext feedback mechanism(PRCFM). This encryption system is a symmetric-key cryptosystem, eliminating the need for transmitting supplementary data beyond the secret keys to the recipient. Notably, the encrypted image maintains identical dimensions to its original counterpart and is fully recoverable. Complete simulation experiments were conducted on a personal computer equipped with MATLAB R2021a, OS Windows 11, 2.60GHz CPU and 16GB RAM. The experimental results indicate that our encryption system, employing a single permutation-diffusion round, efficiently encrypts a 512×512 image in approximately 0.2854 seconds. Leveraging the advantages of the PRCFM, our approach demonstrates superior plaintext sensitivity, achieving an average number of pixels changing rate (NPCR) of 99.6051 and a unified average changed intensity (UACI) of 33.4452. In summary, our work addresses key limitations of contemporary encryption frameworks, exhibiting acceptable performance in both encryption speed and security strength.

Embedding Quantum Random Phase Encoding Arnold Transform for Advanced Image Security

Purpose: This research proposes an improvised version of the image encryption technique by incorporating Quantum Random Phase Encoding with the Arnold Transform to help enhance the strength and non-predictability of the encryption process. In this research work, some ideas gained from quantum-based methods have been brought to use with conventional approaches in image encryption techniques for enhancing their security. Methods: This model represents the basic methodology that underlies the Arnold Transform for scrambling the arrangement of image pixels to mask recognizable structures within quantum random phase encoding to introduce complexity through quantum-generated random phases. Result: The experimental results show much improvement in encryption efficiency. For example, in the case of "Cameraman" and "Lena", MSE parameters are 98.134 and 104.76, respectively; these now go up to 832.01 and 888.78. This implies that the higher decrement of these values 21.17 dB and 23.98 dB to 13.41 dB and 13.33 dB translates into higher distortion with higher security. Meanwhile, UACI and NPCR are also very steady and the mean value is about 0.3356 to 0.3358 and 99.60 to 99.61, which proves that this method has been effective in changing the pixel's value, and sensitive input changes. Novelty: This work is novel due to the introduction of quantum technologies in the classical methodology of image encryption. While classical techniques make use of conventional transforms for scrambling, like the Arnold Transform, this work embeds quantum randomness and intricacy in the process as a means of encoding namely, Quantum Random Phase Encoding.

Design and FPGA Implementation of a Hyper-Chaotic System for Real-time Secure Image Transmission

Recently, chaos theory has been widely used in multimedia and digital communications due to its unique properties that can enhance security, data compression, and signal processing. It plays a significant role in securing digital images and protecting sensitive visual information from unauthorized access, tampering, and interception. In this regard, chaotic signals are used in image encryption to empower the security; that's because chaotic systems are characterized by their sensitivity to initial conditions, and their unpredictable and seemingly random behavior. In particular, hyper-chaotic systems involve multiple chaotic systems interacting with each other. These systems can introduce more randomness and complexity, leading to stronger encryption techniques. In this paper, Hyper-chaotic Lorenz system is considered to design robust image encryption/ decryption system based on master-slave synchronization. Firstly, the rich dynamic characteristics of this system is studied using analytical and numerical nonlinear analysis tools. Next, the image secure system has been implemented through Field-Programmable Gate Arrays (FPGAs) Zedboard Zynq xc7z020-1clg484 to verify the image encryption/decryption directly on programmable hardware Kit. Numerical simulations, hardware implementation, and cryptanalysis tools are conducted to validate the effectiveness and robustness of the proposed system.

Thumbnail-Preserving Encryption Based on Binary–Decimal Hybrid and a Generalized Coupled Logistic Map

Traditional image encryption methods typically make a trade-off between privacy protection and visual usability. To address this issue, a novel thumbnail-preserving encryption scheme is proposed based on a hybrid of decimal and binary pixel values in three pixel groups, utilizing the excellent dynamic characteristics of a generalized coupled logistic map. Theoretically, it is proved that the core algorithm of this scheme is sum-preserving. Experimentally, it is demonstrated that this scheme not only excels in efficiency and resistance to attacks, effectively safeguarding image privacy, but also retains high-quality thumbnails after encryption, achieving both objectives of image privacy protection and visual usability.

A Novel Color Image Encryption Algorithm Based on Hybrid Two-Dimensional Hyperchaos and Genetic Recombination

The transition from text to images as the primary form of information transmission has recently increased the need for secure and effective encryption techniques due to the expanding information dimensions. The color picture encryption algorithm utilizing chaotic mapping is limited by a small chaotic range, unstable chaotic state, and lengthy encryption duration. This study integrates the Ackley function and the Styblinski–Tang function into a novel two-dimensional hyperchaotic map for optimization testing. A randomness test is run on the chaotic sequence created by the system to check that the new chaotic system can better sustain the chaotic state. This study introduces two techniques, genetic recombination and clock diffusion, to simultaneously disperse and mix images at the bit level. This study utilizes chaotic sequences in genetic recombination and clock drift to propose an image encryption technique. The data indicates that the method demonstrates high encryption efficiency. At the same time, the key also successfully passed the NIST randomness test, verifying its sensitivity and randomness. The algorithm’s dependability has been demonstrated and can be utilized for color image encryption.