Correlation Of Pixels Research Articles

PIM-Net: Progressive Inconsistency Mining Network for image manipulation localization

The content authenticity and reliability of digital images have promoted the research on image manipulation localization (IML). Most current deep learning-based methods focus on extracting global or local tampering features for identifying forged regions. These features usually contain semantic information and lead to inaccurate detection results for non-object or incomplete semantic tampered regions. In this study, we propose a novel Progressive Inconsistency Mining Network (PIM-Net) for effective IML. Specifically, PIM-Net consists of two core modules, the Inconsistency Mining Module (ICMM) and the Progressive Fusion Refinement module (PFR). ICMM models the inconsistency between authentic and forged regions at two levels, i.e., pixel correlation inconsistency and region attribute incongruity, while avoiding the interference of semantic information. Then PFR progressively aggregates and refines extracted inconsistent features, which in turn yields finer and pure localization responses. Extensive qualitative and quantitative experiments on five benchmarks demonstrate PIM-Net’s superiority to current state-of-the-art IML methods. Code: https://github.com/ningnbai/PIM-Net.

Chosen Plaintext Attack on Single Pixel Imaging Encryption via Neural Differential Cryptanalysis

AbstractSingle pixel imaging (SPI) shows great potential in encryption by its indirect imaging mechanism. However, there appears to be room for further exploration in the corresponding cryptanalysis. Current studies primarily rely on straightforward end‐to‐end cryptanalysis of plain‐ciphertext pairs, ignoring the fundamental SPI optical path. As a result, the effectiveness of most attacks depends on the training data and the design of network, triggering low certainty and confidence. In this study, an alternative model is proposed to attack multiple SPI encrypting methods based on chosen plaintext attack framework, where arbitrary plaintexts can be encrypted as ciphertexts for cryptanalysis. In terms of the basic SPI setup, it is found that no matter how complicated the patterns are encrypted, the linear relationship between encrypted patterns and intensity always maintain. Thus, specifically, the ciphertext is first differentialized to derive encrypted patterns. By further reconstructing the pixel correlation of these derived patterns, deep learning is employed to correct them. Ultimately, the cracked patterns are used to decrypt plaintexts by conventional correlation. The experiments demonstrate that this method possesses a certain degree of reusability in the SPI encryption with linear propagating characteristic, like pattern‐encrypting class, demonstrating potential for the indirect optical encryption.

An Image Encryption Algorithm Based on Tabu Search and Hyperchaos

In this paper, we propose a digital image encryption scheme based on Tabu Search (TS) algorithm and Chen’s hyperchaos system. First, in order to enhance the security of the algorithm and resist the known-plaintext attack, the key is associated with the ordinary image, and the hash value generated by the ordinary image is used as the initial value of the hyperchaotic system. Moreover, the TS algorithm is used to obtain the optimal subsequence to scramble the sub-block image, which ensures the scrambling effect of the algorithm. In addition, for the sake of minimizing the correlation of neighborhood pixels and strengthening the effect of scrambling, the ordinary image is divided into blocks for scrambling and diffusion. Through simulation and experiments, the key sensitivity, differential attack and pure ciphertext attack are analyzed. Compared with the other encryption schemes, the results verify the effectiveness and reliability of the proposed scheme.

New Colorful Image Encryption Method Using Triple Chaotic Maps and Grey Wolf Optimization (GWO)

A novel image encryption algorithm employing triple chaotic maps has been developed to address the shortcomings of existing methods in terms of security and efficiency. The algorithm leverages the interconnectivity of color channels in images, using distinct keys to disrupt pixel correlations within each channel. The three chaotic maps utilized URUK, WAM, and Nahrain to generate two sets of keys. The first set is used to shuffle pixel positions, creating scrambled channels. Subsequently, the second set is applied to diffuse these scrambled channels independently. A gray wolf optimization (GWO) algorithm is then employed to further optimize the shuffling process, minimizing pixel correlations and enhancing security. The triple chaotic maps of varying orders contribute to the unpredictability and robustness of the cipher image. A comprehensive security analysis, including entropy, correlation coefficients, and attack resistance, demonstrates the superior performance of the proposed method compared to existing image encryption algorithms

Sterilization of image steganography using self-supervised convolutional neural network

Background With the development of steganography technology, lawbreakers can implement covert communication in social networks more easily, exacerbating network security risks. Sterilization of image steganography methods can eliminate secret messages to block the transmission of illegal covert communication. However, existing methods overly rely on cover-stego image pairs and are unable to sanitize unknown image, which reduces stego image blocking rate in social networks. Methods To address the above problems, this paper proposes an effective sterilization of image steganography method using self-supervised convolutional neural network (SS-Net), which does not require any prior knowledge of image steganography schemes. SS-Net includes a purification module and a refinement module. Firstly, the pixel-shuffle down-sampling in purification module is adopted to reduce the spatial correlation of pixels in the stgeo image, and improve the learning mode from supervised learning to self-supervised learning. Secondly, centrally masked convolutions and dilated convolution residual blocks are merged to eliminate secret messages and avoid image quality degradation. Finally, a refinement module is employed to improve image texture details and boundaries. Results A series of experiments show that SS-Net from BOSSbase test sets is able to balance the destruction of secret messages with image quality, achieving 100% blocking rate of stego image. Meanwhile, our method outperforms the state-of-the-art methods in secret messages elimination ability and image quality preserving ability.

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.

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.

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.

Direct and indirect effects of urbanization on vegetation: A survey of Yunnan central urban Economic Circle, China

Urbanization affects vegetation distribution by changing land cover. However, it also significantly changes urban water and heat conditions, affecting vegetation growth and development. Vegetation coverage is an effective indicator of vegetation growth. This study analyzed trends in vegetation coverage over the past 35 years, taking the Yunnan Central Urban Economic Circle as the study area. The study was based on applying the pixel dichotomy model and correlation statistical analysis on joint Landsat 5, 7, and 8 long- term remote sensing data, meteorological, and land use data. The direct and indirect effects of urbanization on vegetation coverage were also further explored by constructing an urbanization impact framework. The results revealed that: (1) The urban area during urbanization from 1986 to 2021 increased by 720.29 km2. There was a continuous decline in vegetation cover in and around urban areas, which intensified with accelerating urbanization, with the effect being more pronounced in suburban areas. (2) There were consistent increasing trends in urbanization’s direct and indirect effects on vegetation over the last 35 years, with average negative and positive effects of − 0.41 and 1.59, respectively. (3) Direct effects could mainly be attributed to the expansion of impervious surfaces, whereas the main indirect effect during the late urbanization period (2011–2020) was increasing average temperature. The average temperature showed a correlation coefficient with urbanization of 0.7767, and this relationship showed seasonal heterogeneity due to the significant growth of urban vegetation in summer and winter. (4) Cities that developed faster showed better environmental planning and construction. The direct and indirect effects of urbanization on vegetation during the early and middle stages were higher in cities developing at slow and moderate rates, with this trend reversing only in the later stages of urbanization. The results of this study can increase understanding of the effect of urbanization on vegetation coverage in the Yunnan Central Urban Economic Circle. They can assist in improving urban green spaces and urban ecological resilience.

A lossless probabilistic image encryption algorithm based on QSD decomposition and matrix transformations

With the development of Internet technology, images have become widely used in people's daily life. However, some images carry significant importance and sensitivity, making the security and protection of digital information more crucial. Standard encryption schemes such as RSA, DES, and AES, which have proven effective for textual data, face limitations when applied to images due to their unique features, such as large data capacity and strong pixel correlation. In recent years, numerous effective image encryption schemes utilizing diverse techniques have been proposed. However, the use of integer decomposition in encryption techniques is still underexplored. This paper introduces a novel, lossless, and probabilistic image encryption scheme designed to enhance the security of sensitive images during transmission and storage. This scheme is based on a new integer decomposition technique and utilizes a key with a very large key space. In the proposed approach, an image is encrypted into a pair of images using quasi-square decomposition and new matrix transformations. Security is further reinforced through the use of orthogonal matrices, which enhance diffusion while ensuring data integrity and preservation. Experimental results demonstrate that the proposed scheme is highly secure against most known attacks and cryptanalysis methods. Additionally, a comparison with various existing image encryption techniques shows the effectiveness of the proposed approach. This research provides a significant contribution by introducing new techniques that leverage integer decomposition, which are highly effective against CPA (Chosen Plaintext Attack) and KPA (Known-Plaintext Attack). Moreover, these techniques enhance the entropy value of encrypted images and improve the values of NPCR and UACI, which help prevent differential attacks. The proposed scheme has the potential to advance applications in secure image transmission, storage, and privacy protection.

Reversible data hiding in encrypted images based on secret sharing and hierarchical embedding

With the increasing emphasis on information security, reversible data hiding technology in encrypted images (RDH-EI) has been widely applied in cloud storage privacy protection in recent years. However, once the data hider is leaked or compromised, the current algorithm of RDH-EI will encounter the challenge of effectiveness and security. In order to tackle this issue, this paper proposes an RDH-EI scheme based on secret sharing and hierarchical embedding. First, an image secret sharing based on (4, 6)-threshold algorithm is proposed. This algorithm aims to combine image encryption and sharing processes to generate six different encrypted images with vertical correlation. Sending them in encrypted form to cloud storage is beneficial for protecting the semantics of cover image. Then, the hierarchical embedding method is suggested to attain a high embedding payload. In this approach, by analyzing the vertical correlation of adjacent pixels, we have designed three mapping rules based on multi-MSB prediction. Among them, case 1 reserves the highest embedding capacity, case 2 follows, and case 3 has no embedding space. Based on this, the paper can adaptively embed secret data. Finally, fully authorized users can extract secret data and restore the original image by decrypting four or more marked encrypted images. Experimental results indicate that our approach surpass some existing RDH-EI approaches in payload, which average payloads in the best case on the BOSSbase and BOWS-2 datasets reach 4.466 bpp and 4.152 bpp, respectively. Additionally, the proposed RDH-EI method transfers and stores image data in a distributed manner, which helps prevent excessive concentration of power by data hiders and misuse of data. Compared to traditional end-to-end RDH-EI schemes, the proposed approach offers higher security.

An Innovative Recompression Scheme for VQ Index Tables

As we move into the digital era, the pace of technological advancement is accelerating rapidly. Network traffic often becomes congested during the transmission of large data volumes. To mitigate this, data compression plays a crucial role in minimizing transmitted data. Vector quantization (VQ) stands out as a potent compression technique where each image block is encoded independently as an index linked to a codebook, effectively reducing the bit rate. In this paper, we introduce a novel scheme for recompressing VQ indices, enabling lossless restoration of the original indices during decoding without compromising visual quality. Our method not only considers pixel correlations within each image block but also leverages correlations between neighboring blocks, further optimizing the bit rate. The experimental results demonstrated the superior performance of our approach over existing methods.

Designing five-dimensional non-degeneracy chaotic system and its application in reversible data hiding

The application of privacy protection techniques combining chaotic systems with data hiding has attracted considerable attention in recent years. However, the existing schemes have been found to be defective in terms of security and embedding capacity. This paper proposes a construction method for a 5-dimensional non-degeneracy chaotic system (5D-NDCS) to address the shortcomings of the previous approach. The 5D-NDCS possesses five positive Lyapunov exponents, and each dimension can generate chaotic sequences with strong randomness. Utilizing the pseudo-random sequence generated by the 5D-NDCS to encrypt the compressed image can enhance the security of the encrypted image. Subsequently, this paper introduces a joint bit-plane compression (JBPC) algorithm, which is based on the chaotic system and pixel value prediction technique. This algorithm effectively utilizes the pixel correlation of the original image to compress the bit sequence sufficiently, thereby reserving the hiding space. Finally, this paper proposes a reversible data hiding in encrypted images (RDHEI) scheme that supports multiple data hiders, based on the JBPC algorithm. The experimental results demonstrate the scheme’s advantages in terms of embedding rate, security, and fault tolerance.

Research on Variable Parameter Color Image Encryption Based on Five-Dimensional Tri-Valued Memristor Chaotic System.

To construct a chaotic system with complex characteristics and to improve the security of image data, a five-dimensional tri-valued memristor chaotic system with high complexity is innovatively constructed. Firstly, a pressure-controlled tri-valued memristor on Liu's pseudo-four-wing chaotic system is introduced. Through analytical methods, such as Lyapunov exponential map, bifurcation map and attractor phase diagram, it is demonstrated that the new system has rich dynamical behaviors with periodic limit rings varying with the coupling parameter of the system, variable airfoil phenomenon as well as transient chaotic phenomenon of chaos-periodic depending on the system parameter and chaos-quasi-periodic depending on the memristor parameter. The system is simulated with dynamic circuits based on Simulink. Secondly, the differently structured synchronous controls of chaotic systems are realized using a nonlinear feedback control method. Finally, based on the newly constructed five-dimensional chaotic system, a variable parameter color image encryption scheme is proposed to iteratively generate varying chaotic pseudo-random sequences by varying the system parameters, which will be used for repetition-free disambiguation, additive modulo left-shift diffusion and DNA encryption for the three components of RGB of the color image after chunking. The simulation results are analyzed by histogram, information entropy, adjacent pixel correlation, etc., and the images are tested using differential attack, noise attack and geometric attack, as well as analyzing the PSNR and SSIM of the decrypted image quality. The results show that the encryption method has a certain degree of security and can be applied to medical, military and financial fields with more complex environmental requirements.

FPGA-enhanced system-on-chip for finger vein-based biometric system using novel DL model

In an era dominated by technology, the imperative for robust personal authentication in electronic information systems becomes increasingly evident. A secure and dependable solution to address this need is biometric authentication. Due to their intrinsic features of being universal, unique, and fraud-resistant, finger vein-based recognition systems have gained importance. Veins provide an efficient barrier against misleading methods since they are buried under the skin and undetectable to human sight. While many researchers focus on advanced technology for finger-vein-based authentication systems, existing research has often overlooked significant challenges, such as short datasets, high computational complexity, and a lack of efficient and lightweight feature descriptors. This paper proposes a unique method for automated Finger Vein Recognition (FVR) based on a fusion model known as “CNN-ViT” for FVR. Transfer learning-based Convolutional Neural Network (CNN) models, such as Inception-V3 and ResNet-50, compute the correlation of adjacent pixels to process texture-based features. Furthermore, shape-based features are processed using the vision transformer (ViT) model to determine the relationship between distant pixels. The combination of these three models enables the learning of textural features based on forms, contributing to more effective finger vein identification. In addition to our databases, we utilize two benchmark databases, FV-USM and SDUMLA-HMT, to validate our experiments. Our proposed approach achieves outstanding accuracy values of 99.95 %, 98.9 %, and 97.78 % on both the benchmark and our datasets. When compared to previous methods, the proposed Deep Learning (DL) model outperforms state-of-the-art models, demonstrating higher recognition rates and accuracy. To prototype the proposed FVR system, a Zynq XCZU4EV UltraScale + Multiprocessor System-On-Chip (MPSoC) was employed. The proposed model exhibits high throughput and competitive power efficiency, making it an excellent choice for scenarios where computing performance is critical, albeit utilizing more power and resources. This was established through a comprehensive examination of FPGA resource utilization and performance metrics.

Lightweight Image Encryption Using a Novel Chaotic Technique for the Safe Internet of Things

Recently, the field of lightweight cryptography (LWC) has emerged in response to the security needs of low-cost, widely used technology. It is essential to implement an encryption approach with access control to give less complex, more flexible, and safe access to sensitive data. In this work, a novel lightweight chaotic encryption approach with fuzzy access control is presented to encrypt light images in the IoT domain, while maintaining image quality. With the aid of multiplexer modeling and information shift register technology, the algorithm’s design combines random and chaotic mapping approach based on a specific password key with a predetermined number of fuzzy logic shifts on the password key for the image pixels. Further, to extract the private key with complexity and boost defense against attacks, a shift register and logical xor combination is employed. The simulation of the proposed model for AVR microcontroller has been done under MATLAB software and the design of various encryption components has been used to implement lightweight mapping. The proposed system has been evaluated in terms of histogram analysis, adjacent pixel correlation analysis, contrast analysis, homogeneity analysis, energy analysis, NIST analysis, error mean square analysis, information entropy, pixel number change rate, integrated mean change intensity, peak signal-to-noise ratio, and time complexity. Remarkably, the proposed technique has demonstrated high efficiency. The simulation results show that the homogeneity, energy, contrast, NPCR, and UACI criteria have improved by 11.5%, 13.1%, 19%, 0.53%, and 0.12%, respectively, compared to other methods in other articles.

A novel CT image segmentation model

Convolutional neural networks (CNNs) can be used for clinical medical image segmentation to improve detection efficiency and accuracy. However, because of the fixed size of convolution kernel and receptive field for existing CNN model, some associated pixel features are ignored and segmentation performance is impaired. Therefore, we propose an effective image segmentation model, COPANet, which uses RepVGG as the backbone and replaces the standard convolution with dilated convolution of multiple kernels to increase the receptive field size, so that COPANet can make full use of pixel correlations and acquire contexture semantic information. We also build skip-connections between global and local features, where a parallel attention (PANet) is employed to extract important location information in the downsampling. PANet is a fused mechanism integrating channel attention with spatial attention in parallel, which can fully extract more global information. We also apply weighted combined loss to reduce the effect of class imbalance of foreground and background pixels on segmentation performance and speed up convergence. In addition, we conduct experiments on 480 cases of clinical CT sinus from Shanghai Tongji Hospital and 236 cases of CT patella fracture from Shanghai Sixth People's Hospital. The evaluation indexes after 5-Fold cross-validation are as follows: Precision is 92.76% and 96.55%, Recall is 88.25% and 57.58%, Specificity is 99.85% and 96.55%, IOU is 91.08% and 77.38%, Hausdorff Distance is 2.8135 and 11.1879, respectively. Compared with the state-of-the-art models, COPANet has higher segmentation accuracy and better generalization performance, which can assist the clinical diagnosis.

Local bit-level image encryption algorithm based on one dimensional zero excluded chaotic map

The exchange of digital images on the internet has become more convenient, but it has also led to increasing security concerns. Image encryption differs from text encryption, as inherent features such as massive data volume and high pixel correlation make it challenging to apply traditional AES and DES methods to images. This paper introduces a novel local bit-level image encryption algorithm based on chaos. Firstly, a new one-dimensional chaos system named the One-Dimensional Zero Excluded Chaotic Map (1D-ZECM) is designed, possessing features such as approximate global chaos, a broad chaos range, and high Lyapunov exponents, making it well-suited for cryptography. To resist brute force attacks, a hash function is employed to generate the encryption system’s key, further enhanced by using the 1D-ZECM to derive the key stream for the cryptographic system. Unlike traditional encryption methods that encrypt all 8 bits of a pixel, this algorithm focuses on the first six bits of each pixel during the encryption process, as the lower two bits contain less image information. In the diffusion process, the key stream generated by the 1D-ZECM is combined with mod and XOR operations to diffuse the rearranged image. Experimental results demonstrate that the proposed encryption algorithm exhibits high security and can resist common attacks. Moreover, when compared to representative algorithms, the proposed algorithm demonstrates better security and efficiency. The encryption algorithm presented in this paper provides a high-quality encrypted output.

Aligning Correlation Information for Domain Adaptation in Action Recognition.

Domain adaptation (DA) approaches address domain shift and enable networks to be applied to different scenarios. Although various image DA approaches have been proposed in recent years, there is limited research toward video DA. This is partly due to the complexity in adapting the different modalities of features in videos, which includes the correlation features extracted as long-range dependencies of pixels across spatiotemporal dimensions. The correlation features are highly associated with action classes and proven their effectiveness in accurate video feature extraction through the supervised action recognition task. Yet correlation features of the same action would differ across domains due to domain shift. Therefore, we propose a novel adversarial correlation adaptation network (ACAN) to align action videos by aligning pixel correlations. ACAN aims to minimize the distribution of correlation information, termed as pixel correlation discrepancy (PCD). Additionally, video DA research is also limited by the lack of cross-domain video datasets with larger domain shifts. We, therefore, introduce a novel HMDB-ARID dataset with a larger domain shift caused by a larger statistical difference between domains. This dataset is built in an effort to leverage current datasets for dark video classification. Empirical results demonstrate the state-of-the-art performance of our proposed ACAN for both existing and the new video DA datasets.

Multiobjective Optimization of Chaotic Image Encryption Based on ABC Algorithm and DNA Coding

As digital communication and storage continue to expand, the protection of image privacy information becomes increasingly critical. To safeguard sensitive visual information from unauthorized access, this paper proposes a novel image encryption scheme that integrates multiobjective Artificial Bee Colony (ABC) optimization algorithm and DNA coding. Multiple evaluation metrics including correlation relationship, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), and information entropy are collaboratively optimized by the ABC algorithm. The proposed method begins with the application of the SHA-256 algorithm to generate keys and random sequences using chaotic systems. These sequences are then employed for shuffling, DNA coding, decoding, and diffusion, generating initial encrypted images. Subsequently, the encrypted images serve as individuals within the ABC algorithm to determine optimal parameters of the chaotic systems and the best ciphertext image. Simulation experiments demonstrate that the ciphertext images achieved excellent results in information entropy, pixel correlation coefficient, NPCR, and UACI. The integration of the multiobjective ABC optimization algorithm with DNA coding in our proposed image encryption scheme results in heightened security, as evidenced by superior performance in various metrics.