Image Encryption Research Articles

Water-Adapter: adapting the segment anything model for surface water extraction in optical very-high-resolution remotely sensed imagery

ABSTRACT Surface water extraction (SWE) from very-high-resolution optical remote sensing images is crucial yet challenging due to the complex spectral variability of water bodies. To address this, we propose Water-Adapter, a novel method that enhances SWE by leveraging the Segment Anything Model (SAM), a large-scale image segmentation framework. Our approach introduces a task-specific input module that utilizes explicit visual prompting by focusing the tunable parameters on the visual content of each image, specifically leveraging features from frozen patch embeddings and low-frequency components. By freezing most of SAM’s image encoder parameters and incorporating a few domain-specific trainable adapters, Water-Adapter effectively integrates remote sensing knowledge into SAM, significantly improving segmentation performance with minimal computational overhead. Extensive experiments on the GLH-Water dataset demonstrate that Water-Adapter outperforms state-of-the-art methods in both accuracy and efficiency.

Multiple Color Image Encryption System Based on Hybrid Digital/Optical Techniques and Assisted by 18D Memristive Chaos

Multiple Color Image Encryption System Based on Hybrid Digital/Optical Techniques and Assisted by 18D Memristive Chaos

Enhancing the Security and Efficiency of Biomedical Image Encryption through a Novel Hyper-Chaotic Logistic Map

Enhancing the Security and Efficiency of Biomedical Image Encryption through a Novel Hyper-Chaotic Logistic Map

Selective Medical Image Encryption and Compression Based on DCT, 3D_Att_ResU-Net and 4D Hyperchaotic Map Schemes

Selective Medical Image Encryption and Compression Based on DCT, 3D_Att_ResU-Net and 4D Hyperchaotic Map Schemes

Hardware Area Efficiently and Real-Time FPGA Implementation of PHMMRGB

For encryption applications on embedded systems, operating in real-time while using minimal system resources is essential. It is expected that efficient and rapid encryption of high-resolution images is to be accomplished with limited hardware resources. Therefore, in order to ensure the desired efficiency of encryption of high-resolution images, the system must possess a digital architecture capable of achieving high speeds with minimal hardware resources. This study considered the limitations of a hardware architecture for an image encryption algorithm based on the Profile Hidden Markov Model called PHMMRGB. The proposed architecture is relatively simple in comparison to its alternatives. The hardware architecture, designed with the objective of using minimal resources, has been implemented on an FPGA. Based on the results of the proposed architecture, it is believed that the implementation of the specified method on an FPGA would yield high efficiency. Experiments conducted using large-sized satellite images confirmed this expectation.

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.

Global Exponential Synchronization of Delayed Quaternion-Valued Neural Networks via Decomposition and Non-Decomposition Methods and Its Application to Image Encryption

With the rapid advancement of information technology, digital images such as medical images, grayscale images, and color images are widely used, stored, and transmitted. Therefore, protecting this type of information is a critical challenge. Meanwhile, quaternions enable image encryption algorithm (IEA) to be more secure by providing a higher-dimensional mathematical system. Therefore, considering the importance of IEA and quaternions, this paper explores the global exponential synchronization (GES) problem for a class of quaternion-valued neural networks (QVNNs) with discrete time-varying delays. By using Hamilton’s multiplication rules, we first decompose the original QVNNs into equivalent four real-valued neural networks (RVNNs), which avoids non-commutativity difficulties of quaternions. This decomposition method allows the original QVNNs to be studied using their equivalent RVNNs. Then, by utilizing Lyapunov functions and the matrix measure method (MMM), some new sufficient conditions for GES of QVNNs under designed control are derived. In addition, the original QVNNs are examined using the non-decomposition method, and corresponding GES criteria are derived. Furthermore, this paper presents novel results and new insights into GES of QVNNs. Finally, two numerical verifications with simulation results are given to verify the feasibility of the obtained criteria. Based on the considered master–slave QVNNs, a new IEA for color images Mandrill (256 × 256), Lion (512 × 512), Peppers (1024 × 1024) is proposed. In addition, the effectiveness of the proposed IEA is verified by various experimental analysis. The experiment results show that the algorithm has good correlation coefficients (CCs), information entropy (IE) with an average of 7.9988, number of pixels change rate (NPCR) with average of 99.6080%, and unified averaged changed intensity (UACI) with average of 33.4589%; this indicates the efficacy of the proposed IEAs.

Metal-to-insulator phase transition of molybdenum oxide for reversible writing and erasing information encryption

Abstract We have reported a theoretical device design for reversible writing and erasing information encryption using a simple two layered structure, which consists of a MoOx layer and a SiO2 space layer on a silver substrate. The MoOx layer can be switched between two phases, i.e., metallic MoO2 and insulating MoO3. The phase transition of MoOx possesses very complicated processes, which makes it advantageous for information decryption. Generally, in the proposed structure with metallic MoO2, various colors can be generated due to the F-P enhanced absorption at wavelengths where construction interference occurs. However, owing to the low loss of insulating MoO3 in visible region, the absorption is low though out the whole visible region, thus all colors can be erased to white. Therefore, the color image information can be written and erased with our proposed structure by switching MoOx between phases of metallic MoO2 and insulating MoO3. The proposed image encryption device can be acquired easily by masking and coating processes. We believe our results provides a new way in designing encryption devices.

Efficient algorithms for perturbed symmetrical Toeplitz-plus-Hankel systems

This paper investigates a specific class of perturbed Toeplitz-plus-Hankel matrices. We introduce two novel algorithms designed for perturbed symmetrical centrosymmetrical Toeplitz-plus-Hankel systems, offering reduced computational time. Furthermore, we present applications of image encryption and decryption based on these algorithms. Through numerical experiments, we demonstrate the effectiveness of our proposed algorithms.

Grounded situation recognition under data scarcity

Grounded Situation Recognition (GSR) aims to generate structured image descriptions. For a given image, GSR needs to identify the key verb, the nouns corresponding to roles, and their bounding-box groundings. However, current GSR research demands numerous meticulously labeled images, which are labor-intensive and time-consuming, making it costly to expand detection categories. Our study enhances model accuracy in detecting and localizing under data scarcity, reducing dependency on large datasets and paving the way for broader detection capabilities. In this paper, we propose the Grounded Situation Recognition under Data Scarcity (GSRDS) model, which uses the CoFormer model as the baseline and optimizes three subtasks: image feature extraction, verb classification, and bounding-box localization, to better adapt to data-scarce scenarios. Specifically, we replace ResNet50 with EfficientNetV2-M for advanced image feature extraction. Additionally, we introduce the Transformer Combined with CLIP for Verb Classification (TCCV) module, utilizing features extracted by CLIP’s image encoder to enhance verb classification accuracy. Furthermore, we design the Multi-source Verb-Role Queries (Multi-VR Queries) and the Dual Parallel Decoders (DPD) modules to improve the accuracy of bounding-box localization. Through extensive comparative experiments and ablation studies, we demonstrate that our method achieves higher accuracy than mainstream approaches in data-scarce scenarios. Our code will be available at https://github.com/Zhou-maker-oss/GSRDS.

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.

An image encryption algorithm for colour images based on a cellular neural network and the Chua's chaotic system

In the internet era, image encryption technology plays a crucial role in ensuring the security of image information. Aiming at the problem that low-dimensional chaotic encryption has a relatively small key space and is vulnerable to chosen-plain attacks, this paper proposed a novel colour image block encryption algorithm based on the cellular neural network and the Chua's chaotic system. Firstly, the plain is separated into three primary colours. And Fourier transform is applied to achieve optical transformation, generating six chaotic sequences iteratively. Secondly, the chaotic sequences were combined in pairs. And then combined with the plain to form three sequences, which were used in the diffusion process. Finally, the improved Chua's chaotic system is used to scramble the image. Both diffusion and scrambling are performed synchronously during encryption. Through the above operation, we get the final colour encrypted picture. The experimental results show that the algorithm has excellent encryption effect and can resist common attacks.

Secure authentication and encryption via diffraction imaging-based encoding and vector decomposition

Abstract Traditional optical encryption systems have security risks due to their linearity and usually encounter problems such as the heavy burden of key transmission and storage. This paper proposes a novel security-enhanced optical image authentication and encryption framework that combines diffractive imaging-based encryption with the vector decomposition algorithm (VDA). Chaotic random phase masks (CRPMs) are used to encrypt data for authentication via VDA, and a pair of complementary binary matrix keys are utilized to extract information from the encrypted data to generate ciphertext. During the authentication and decryption processes, a sparse reference image is reconstructed from the ciphertext for verification. If the authentication is successful, image decryption can be executed using a key-assisted phase retrieval algorithm. The employment of nonlinear VDA, an additional layer of authentication, and the use of CRPMs and binary matrix keys enhance security and address key burden concerns. Simulation results demonstrate the feasibility, effectiveness, and security of the scheme.

A novel one-dimensional Cosine within Sine chaotic map and novel permutation–diffusion based medical image encryption

A novel one-dimensional Cosine within Sine chaotic map and novel permutation–diffusion based medical image encryption

Quantum Color Image Encryption: Dual Scrambling Scheme based on DNA Codec and Quantum Arnold Transform

Abstract In the field of Internet, image is of great significance to information transmission. Meanwhile, how to ensure and improve its security has become the focus of research in the world today. We combine DNA codec with Quantum Arnold Transform (QArT) to propose a new double encryption algorithm for quantum color images. to improve the security and robustness of image encryption.Firstly, we utilize the biological characteristics of DNA codecs to perform encoding and decoding operations on pixel color information in quantum color images, and achieve pixel-level diffusion. Secondly, we use QArT to scramble the position information of quantum images and use the operated image as the key matrix for quantum XOR operations. All quantum operations in this paper are reversible, so the decryption operation of the ciphertext image can be realized by the reverse operation of the encryption process. We conduct simulation experiments on encryption and decryption using three color images of ”Monkey”, ”Flower” and ”House”. The experimental results show that the peak value and correlation of the encrypted images on the histogram have good similarity, and the average NPCR of RGB three-channel is 99.61%, the average UACI is 33.41%, and the average information entropy is about 7.9992. In addition, the robustness of the proposed algorithm is verified by the simulation of noise interference in the actual scenario.

A dynamic authorizable ciphertext image retrieval algorithm based on security neural network inference.

In this paper, we propose a dynamic authorizable ciphertext image retrieval scheme based on secure neural network inference that effectively enhances the security of image retrieval while preserving privacy. To ensure the privacy of the original image and enable feature extraction without decryption operations, we employ a secure neural network for feature extraction during the index construction stage of encrypted images. Additionally, we introduce a dynamic authenticatable ciphertext retrieval algorithm to enhance system flexibility and security by enabling users to quickly and flexibly retrieve authorized images. Experimental results demonstrate that our scheme guarantees data image privacy throughout the entire process from upload to retrieval compared to similar literature schemes. Furthermore, our scheme ensures data availability while maintaining security, allowing users to conveniently perform image retrieval operations. Although overall efficiency may not be optimal according to experimental results, our solution satisfies practical application needs in cloud computing environments by providing an efficient and secure image retrieval solution.

Quantum image encryption algorithm based on 3D-BNM chaotic map

Quantum image encryption algorithm based on 3D-BNM chaotic map

Dynamics analysis of fractional-order extended neuron model under electromagnetic field and application to image encryption

Dynamics analysis of fractional-order extended neuron model under electromagnetic field and application to image encryption

A Multilayer Nonlinear Permutation Framework and Its Demonstration in Lightweight Image Encryption

As information systems become more widespread, data security becomes increasingly important. While traditional encryption methods provide effective protection against unauthorized access, they often struggle with multimedia data like images and videos. This necessitates specialized image encryption approaches. With the rise of mobile and Internet of Things (IoT) devices, lightweight image encryption algorithms are crucial for resource-constrained environments. These algorithms have applications in various domains, including medical imaging and surveillance systems. However, the biggest challenge of lightweight algorithms is balancing strong security with limited hardware resources. This work introduces a novel nonlinear matrix permutation approach applicable to both confusion and diffusion phases in lightweight image encryption. The proposed method utilizes three different chaotic maps in harmony, namely a 2D Zaslavsky map, 1D Chebyshev map, and 1D logistic map, to generate number sequences for permutation and diffusion. Evaluation using various metrics confirms the method’s efficiency and its potential as a robust encryption framework. The proposed scheme was tested with 14 color images in the SIPI dataset. This approach achieves high performance by processing each image in just one iteration. The developed scheme offers a significant advantage over its alternatives, with an average NPCR of 99.6122, UACI of 33.4690, and information entropy of 7.9993 for 14 test images, with an average correlation value as low as 0.0006 and a vast key space of 2800. The evaluation results demonstrated that the proposed approach is a viable and effective alternative for lightweight image encryption.

CLIP-DFGS: A Hard Sample Mining Method for CLIP in Generalizable Person Re-Identification

Recent advancements in pre-trained vision-language models like CLIP have shown promise in person re-identification (ReID) applications. However, their performance in generalizable person re-identification tasks remains suboptimal. The large-scale and diverse image-text pairs used in CLIP's pre-training may lead to a lack or insufficiency of certain fine-grained features. In light of these challenges, we propose a hard sample mining method called DFGS (Depth-First Graph Sampler), based on depth-first search, designed to offer sufficiently challenging samples to enhance CLIP's ability to extract fine-grained features. DFGS can be applied to both the image encoder and the text encoder in CLIP. By leveraging the powerful cross-modal learning capabilities of CLIP, we aim to apply our DFGS method to extract challenging samples and form mini-batches with high discriminative difficulty, providing the image model with more efficient and challenging samples that are difficult to distinguish, thereby enhancing the model's ability to differentiate between individuals. Our results demonstrate significant improvements over other methods, confirming the effectiveness of DFGS in providing challenging samples that enhance CLIP's performance in generalizable person re-identification.