Image Encryption Research Articles

Asymmetric Optical Scanning Holography Encryption with Elgamal Algorithm

This paper proposes an asymmetric scanning holography cryptosystem based on the Elgamal algorithm. The method encodes images with sine and cosine holograms. Subsequently, each hologram is divided into a signed bit matrix and an unsigned hologram matrix, both encrypted using the sender’s private key and the receiver’s public key. The resulting ciphertext matrices are then transmitted to the receiver. Upon receipt, the receiver decrypts the ciphertext matrices using their private key and the sender’s public key. We employ an asymmetric single-image encryption method for key management and dispatch for securing imaging and transmission. Furthermore, we conducted a sensitivity analysis of the encryption system. The image encryption metrics, including histograms of holograms, adjacent pixel correlation, image correlation, the peak signal-to-noise ratio, and the structural similarity index, were also examined. The results demonstrate the security and stability of the proposed method.

FastComposer: Tuning-Free Multi-subject Image Generation with Localized Attention

AbstractDiffusion models excel at text-to-image generation, especially in subject-driven generation for personalized images. However, existing methods are inefficient due to the subject-specific fine-tuning, which is computationally intensive and hampers efficient deployment. Moreover, existing methods struggle with multi-subject generation as they often blend identity among subjects. We present FastComposer which enables efficient, personalized, multi-subject text-to-image generation without fine-tuning. FastComposer uses subject embeddings extracted by an image encoder to augment the generic text conditioning in diffusion models, enabling personalized image generation based on subject images and textual instructions with only forward passes. To address the identity blending problem in the multi-subject generation, FastComposer proposes cross-attention localization supervision during training, enforcing the attention of reference subjects localized to the correct regions in the target images. Naively conditioning on subject embeddings results in subject overfitting. FastComposer proposes delayed subject conditioning in the denoising step to maintain both identity and editability in subject-driven image generation. FastComposer generates images of multiple unseen individuals with different styles, actions, and contexts. It achieves 300$$\times $$ × –2500$$\times $$ × speedup compared to fine-tuning-based methods and requires zero extra storage for new subjects. FastComposer paves the way for efficient, personalized, and high-quality multi-subject image creation. Code, model, and dataset are available here (https://github.com/mit-han-lab/fastcomposer).

A novel image encryption method based on the cycle replacement

For the bit-level image encryption algorithms, pixel values and positions can be changed simultaneously. The operation can enhance the security of image encryption but will require the complicated calculations. Therefore, high security and suitable computation for a new algorithm are needed to be considered. In this paper, a novel image encryption algorithm, which combines the bit-level encryption and the pixel-level encryption methods, is proposed based on the cycle replacement. Firstly, a new 2-dimensional (2D) map with a hyperbolic cosine function (2D-Cosh map) is introduced, which has rich and complex dynamics. Based on the chaotic characteristic of the map, an image encryption algorithm is introduced via the substitution of bit of pixels which can scramble the pixels, and change the image pixel positions effectively. Numerical simulation and security analysis are used to demonstrate the effectiveness and feasibility of the algorithm. From which we can see that the correlation coefficients are almost 0, average entropy = 7.9973, average NPCR = 99.6104%, and average UACI = 33.4664%. It is clear that the algorithm is resistant to differential attacks, interference attacks, and can reduce the correlation of adjacent pixels of the encrypted image greatly. Meanwhile, the algorithm has no limit for the size of a color image in the process of the encryption.

Ultrathin near-infrared transmitting films enabled by deprotonation-induced intramolecular charge transfer of a dopant

Near-infrared transparent films demonstrate important applications in many fields, but how to eliminate light interference from ultraviolet-visible region and how to tackle the trade-off effect between film thickness and transmittance remain as challenges. Herein, we report a near-infrared transparent film that achieves high-efficient combination of thin thickness (16 μm), suitable cut-off wavelength (890 nm), and ideal transmittance (TNIR > 90%, TVis < 1%). Moreover, the film is photo-chemically stable, heating resistance and moisture insensitive. The key component of the film is a complex of a specially designed boron compound containing a perylene monoimide unit (PMI-CBN) with an organic base 1,8-diazabicyclo[5,4,0]undec-7-ene. The complex depicts red-shifted absorption from 709 to 943 nm owing to deprotonation of the N-H group of PMI-CBN. Dispersion of the complex in polymethyl methacrylate results in the high-performance film. As demos, the film is successfully used for night vision imaging and information encryption.

A Framework for Agricultural Intelligent Analysis Based on a Visual Language Large Model

Smart agriculture has become an inevitable trend in the development of modern agriculture, especially promoted by the continuous progress of large language models like chat generative pre-trained transformer (ChatGPT) and general language model (ChatGLM). Although these large models perform well in general knowledge learning, they still have certain limitations and errors when facing agricultural professional knowledge about crop disease identification, growth stage judgment, and so on. Agricultural data involves images and texts and other modalities, which play an important role in agricultural production and management. In order to better learn the characteristics of different modal data in agriculture, realize cross-modal data fusion, and thus understand complex application scenarios, we propose a framework AgriVLM that uses a large amount of agricultural data to fine-tune the visual language model to analyze agricultural data. It can fuse multimodal data and provide more comprehensive agricultural decision support. Specifically, it utilizes Q-former as a bridge between an image encoder and a language model to achieve a cross-modal fusion of agricultural images and text data. Then, we apply a Low-Rank adaptive to fine-tune the language model to achieve an alignment between agricultural image features and a pre-trained language model. The experimental results prove that AgriVLM demonstrates great performance in crop disease recognition and growth stage recognition, with recognition accuracy exceeding 90%, demonstrating its capability to analyze different modalities of agricultural data.

Data encryption/decryption and medical image reconstruction based on a sustainable biomemristor designed logic gate circuit

Memristors are considered one of the most promising new-generation memory technologies due to their high integration density, fast read/write speeds, and ultra-low power consumption. Natural biomaterials have attracted interest in integrated circuits and electronics because of their environmental friendliness, sustainability, low cost, and excellent biocompatibility. In this study, a sustainable biomemristor with Ag/mugwort:PVDF/ITO structure was prepared using spin-coating and magnetron sputtering methods, which exhibited excellent durability, significant resistance switching (RS) behavior and unidirectional conduction properties when three metals were used as top electrode. By studying the conductivity mechanism of the device, a charge conduction model was established by the combination of F-N tunneling, redox, and complexation reaction. Finally, the novel logic gate circuits were constructed using the as-prepared memristor, and further memristor based encryption circuit using 3-8 decoder was innovatively designed, which can realize uniform rule encryption and decryption of medical information for data and medical images. Therefore, this work realizes the integration of memristor with traditional electronic technology and expands the applications of sustainable biomemristors in digital circuits, data encryption, and medical image security.

Optical phase image encryption using stokes parameters and singular value decomposition

Abstract In this paper, we propose an optical asymmetric phase image encryption method in which the vectorial light field is used to encode the data. In transverse plane, the vectorial light field has spatially varying polarization distributions where we are allowed to have a greater number of degrees of freedom. In this scheme, the input image is first phase encoded and then modulated by a phase encrypting key, synthesized from the speckles obtained by the scattering of Hermite–Gaussian beams. The modulated image is further processed using fractional Fourier transform with a specific order (α). A pixel scrambling operator is utilized to increase the randomness to further enhance the security and singular value decomposition approach is employed to add the nonlinearity in the encryption process. Now, the stokes parameters, i.e. S 1 and S 2 are calculated using the light intensities correspond to different polarizations. S 1 is used as the encrypted image for transmission and S 2 is reserved as one of the private decryption keys. The robustness of the proposed technique is tested against various existing attacks, such as known plaintext attack, chosen plaintext attack, and contamination attacks. Numerically simulated results validate the effectiveness and efficiency of the proposed method.

An Intelligent Image Encryption Scheme Based on Hyperchaotic Map and Dynamic DNA Encoding

An Intelligent Image Encryption Scheme Based on Hyperchaotic Map and Dynamic DNA Encoding

Performance Analysis of symmetric and asymmetric Encryption Algorithms Based on File, Image and Video

The rapid evolution of digital technology has exponentially amplified the generation and sharing of diverse digital data, notably files, images, and videos, over the internet, intensifying the critical need for enhanced security measures to safeguard these prevalent data types. This research presents a comprehensive analysis of various encryption algorithms applied to different data types - files, images, and videos. The study categorizes encryption algorithms into symmetric and asymmetric types, with examples including AES, DES, Triple DES, RSA, Diffie-Hellman, and ECC. The paper further explores specific algorithms used for file, image, and video encryption. The objective is to identify the most efficient encryption algorithm for each data type, thereby enhancing data security in the digital age. The paper emphasizes that while encryption is a crucial tool for data security, it should be used in conjunction with other security measures for comprehensive protection.

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.

A new color image encryption algorithm based on the memristor hyperchaos system and Rubik’s cube theory

A new color image encryption algorithm based on the memristor hyperchaos system and Rubik’s cube theory

Performance analysis of multiuser mmWave DCT-spread CP-Less OFDM communication system

In this paper, we propose a framework for multiuser mmWave DCT-Spread CP-less OFDM communication system and analyze it comprehensively. Due to excessive cyclic prefix (CP) usage in conventional multicarrier systems, spectral efficiency reduces and increases transmission latency. Our proposed system enhances spectral efficiency and reduces transmission latency. We introduce an image encryption algorithm based on DNA encoding combined with a chaotic map is introduced to enhance physical layer security (PLS). The impact of block diagonalization (BD) channel precoding for multiuser interference (MUI) reduction and designed subcarrier mapping with time-domain Tukey windowing for out-of-band (OOB) power emission reduction are investigated. In addition, the paper studies the applicability of discrete cosine transforms (DCT)-Spreading for peak-to-average power ratio (PAPR) reduction. Simulation results demonstrate that Turbo and Repeat and Accumulate (RA) channel coding and minimum mean square error (MMSE) and Cholesky decomposition (CD)-based zero-forcing (ZF) signal detection schemes improve bit error rate (BER) performance of the proposed system for different users.

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.

An image encryption scheme based on an improved memristive neuron chaotic system

With fast-developing Internet and communication, the security transmission of image in network has become a research highlight. So far, researchers have designed a lot of image encryption methods based on chaotic models, some of them are not secure enough. To enhance security of image transmission on the network, in this paper, an image encryption method is developed from a 3D memeristive FitzHugh-Nagumo (FHN) neuron. First, a 3D memeristive FHN model is obtained by connecting a memeristor into a 2D FHN model, and dynamics for 3D FHN model are estimated by applying phase diagrams, bifurcation and Lyapunov exponent. Then, an image encryption algorithm is proposed by using this 3D model. Finally, security of encryption algorithm is estimated. Simulation results confirm the effectiveness of encryption scheme.

Improved Chua’s chaotic system with a novel memristor: generating multi-scroll hidden attractors and applications in image encryption

In this paper, a new method for generating multi-scroll chaotic attractors by introducing a new memristor model into an improved Chua’s system is presented. The dynamic characteristics of the system are analyzed, including equilibria and stability, bifurcation diagrams, Lyapunov exponents and phase diagrams. Strikingly, it can exhibit three-scroll, four-scroll and five-scroll hidden attractors by only changing one system parameter and different shapes of chaotic attractors coexist with the same parameter. Furthermore, the high randomness of the system is verified through 0–1 test and NIST test. By using the high randomness of the system, an image encryption algorithm (IEA) is designed to guarantee the secure and efficient transmission of digital images. This IEA uses whirlpool transformation to scramble pixel positions and DNA computation to diffuse pixel values. The simulation and performance analysis results indicate that the proposed IEA has high security and reliable encryption performance, which in turn confirms the availability of the new memristor-based Chua’s chaotic system.

A new 2D cross hyperchaotic Sine-modulation-Logistic map and its application in bit-level image encryption

Chaotic theory is being increasingly researched for application in image encryption scheme (IES). This paper presents a novel two-dimensional cross hyperchaotic Sine-modulation-Logistic map (2D-CHSLM) based on the famous Sine and Logistic maps. The hyperchaotic behaviors of 2D-CHSLM are demonstrated through bifurcation diagrams, trajectory plots, Lyapunov exponents, histogram, correlation dimension, sensitivity to initial conditions, sample entropy, 0–1 test, and NIST 800–22 test. A new bit-level IES with traditional an encryption structure is proposed utilizing 2D-CHSLM. The 2D-CHSLM-IES performs 2D-CHSLM-based bit-level confusion using a zigzag transform and 2D-CHSLM-based cross coupled diffusion operations to generate a highly secure cipher-image. Simulations and security analyses, including key space, key sensitivity, histogram, correlation, information entropy, encryption quality, known-plain and chosen-plain attacks, floating Frequency, differential attack, noise and data loss attacks, demonstrate the robust security of the proposed 2D-CHSLM-IES.

A novel multi-layer image encryption algorithm based on 2D drop-wave function

A novel multi-layer image encryption algorithm based on 2D drop-wave function

A threefold chaotic image encryption technique for efficacious image data protection

With the profound use of digital contents in education and social media, multimedia content have become a prevalent means of communication and with such rapid increase, information security is still a major concern. This paper provides a summary of the techniques of securing image data focusing primarily on encryption. This paper additionally also explores the contribution of chaos theory in the domain of image encryption and applies a threefold chaotic map sequence to design an image encryption technique. This chaos-based encryption technique renders characteristics like sensitivity, determinacy, and ergodicity, and hence provides a better trade-off between security and computational speed. The efficacy of proposed algorithm is also evaluated based on well-known statistical metrics like Number of Changing Pixel Rate (NPCR), Unified Average Changing Intensity (UACI), Information Entropy, local Entropy, Correlation analysis, key sensitivity analysis, and NIST Randomness test. Furthermore, the key sensitivity analysis is also discussed which is a major part of any encryption algorithm analysis.

Research on multi-sequence MR image synthesis CT algorithm based on unsupervised method

BackgroundUsing multi-sequence MRI to synthesize CT, combining different MRI sequences, and processing synthetic CT (sCT) based on unsupervised algorithms, to conduct more in-depth and comprehensive research on MR-based radiotherapy planning. MethodsFirst, we compare and analyze the effects of single-sequence MRI synthesis to CT to determine the quality of the synthesis and find a set of optimal sequence combinations. In addition, the study introduces a Staged Multi-Sequence Fusion Network (SMSF-Net) that employs an unsupervised approach to effectively fuse information from multiple MRI sequences. The staged sequence fusion module can efficiently extract information from different sequences and combine complementary information from different sequences. Finally, image encoding is processed using a multi-layer convolutional network to generate synthetic CT images. ResultsIn this study, the mean absolute error (MAE), root mean square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) of the SMSF-Net are 18.85, 84.62, 34.31, and 0.962, which are superior to other single-sequence models and multi-sequence models. Experiments show that the improvement of SMSF-Net is statistically significant. In the qualitative comparison experiment, sCT synthesized by SMSF-Net is superior to other models in detail in the synthesis of complex areas of bones and nasal cavities. ConclusionThis study proposes a Staged SMSF-Net. As a multi-sequence MRI synthesis sCT algorithm based on unsupervised learning, SMSF-Net can use the complementary information between multiple sequences to improve the quality of synthetic sCT. This study has reference value for MRI-only radiotherapy planning.

Global quaternion generalized minimal residual method for generalized coupled Sylvester quaternion matrix equations with application to colour image encryption and decryption

ABSTRACT Sylvester matrix equations play important roles in control and system theory. We consider a class of the generalized coupled Sylvester quaternion matrix equations, which includes a lot of special matrix equations such as Stein matrix equation, Lyapunov matrix equation and Sylvester matrix equation on the quaternion skew field. First, the quaternion Krylov subspace and its F-orthogonal basis are constructed via a new quaternion matrix product and the global quaternion Arnoldi process. Then a global quaternion generalized minimal residual (Gl-QGMRES) method is proposed for solving the generalized coupled Sylvester quaternion matrix equations. The convergence of the proposed method is theoretically analysed. Finally, the effectiveness and feasibility of the proposal are verified by some numerical experiments. As an application of Gl-QGMRES method, we establish a kind of colour image encryption and decryption by embedding watermark images.