A Color Image Encryption Scheme Based on Singular Values and Chaos

The security of digital images transmitted via the Internet or other public media is of the utmost importance.Image encryption is a method of keeping an image secure while it travels across a non-secure communication medium where it could be intercepted by unauthorized entities.This study provides an approach to color image encryption that could find practical use in various contexts.The proposed method,which combines four chaotic systems,employs singular value decomposition and a chaotic sequence,making it both secure and compression-friendly.The unified average change intensity,the number of pixels’change rate,information entropy analysis,correlation coefficient analysis,compression friendliness,and security against brute force,statistical analysis and differential attacks are all used to evaluate the algorithm’s performance.Following a thorough investigation of the experimental data,it is concluded that the proposed image encryption approach is secure against a wide range of attacks and provides superior compression friendliness when compared to chaos-based alternatives.

A color image encryption algorithm based on hyperchaotic map and DNA mutation

We devise a color image encryption scheme via combining hyperchaotic map,cross-plane operation and gene theory.First,the hyperchaotic map used in the encryption scheme is analyzed and studied.On the basis of the dynamics of hyperchaotic map,a color image encryption scheme is designed.At the end of the encryption process,a DNA mutation operation is used to increase the encoding images’randomness and to improve the encryption algorithm’s security.Finally,simulation experiments,performance analysis,and attack tests are performed to prove the effectiveness and security of the designed algorithm.This work provides the possibility of applying chaos theory and gene theory in image encryption.

An Efficient Text Recognition System from Complex Color Image for Helping the Visually Impaired Persons

The challenge faced by the visually impaired persons in their day-today lives is to interpret text from documents.In this context,to help these people,the objective of this work is to develop an efficient text recognition system that allows the isolation,the extraction,and the recognition of text in the case of documents having a textured background,a degraded aspect of colors,and of poor quality,and to synthesize it into speech.This system basically consists of three algorithms:a text localization and detection algorithm based on mathematical morphology method(MMM);a text extraction algorithm based on the gamma correction method(GCM);and an optical character recognition(OCR)algorithm for text recognition.A detailed complexity study of the different blocks of this text recognition system has been realized.Following this study,an acceleration of the GCM algorithm(AGCM)is proposed.The AGCM algorithm has reduced the complexity in the text recognition system by 70%and kept the same quality of text recognition as that of the original method.To assist visually impaired persons,a graphical interface of the entire text recognition chain has been developed,allowing the capture of images from a camera,rapid and intuitive visualization of the recognized text from this image,and text-to-speech synthesis.Our text recognition system provides an improvement of 6.8%for the recognition rate and 7.6%for the F-measure relative to GCM and AGCM algorithms.

Detecting Double JPEG Compressed Color Images via an Improved Approach

Detecting double Joint Photographic Experts Group (JPEG) compressionfor color images is vital in the field of image forensics. In previousresearches, there have been various approaches to detecting double JPEGcompression with different quantization matrices. However, the detectionof double JPEG color images with the same quantization matrix is stilla challenging task. An effective detection approach to extract features isproposed in this paper by combining traditional analysis with ConvolutionalNeural Networks (CNN). On the one hand, the number of nonzero pixels andthe sum of pixel values of color space conversion error are provided with 12-dimensional features through experiments. On the other hand, the roundingerror, the truncation error and the quantization coefficient matrix are used togenerate a total of 128-dimensional features via a specially designed CNN. Insuch aCNN, convolutional layers with fixed kernel of 1×1 and Dropout layersare adopted to prevent overfitting of the model, and an average pooling layeris used to extract local characteristics. In this approach, the Support VectorMachine (SVM) classifier is applied to distinguishwhether a given color imageis primarily or secondarily compressed. The approach is also suitable for thecase when customized needs are considered. The experimental results showthat the proposed approach is more effective than some existing ones whenthe compression quality factors are low.

An Optimized Implementation of a Novel Nonlinear Filter for Color Image Restoration

Image processing is becoming more popular because images are being used increasingly in medical diagnosis,biometric monitoring,and character recognition.But these images are frequently contaminated with noise,which can corrupt subsequent image processing stages.Therefore,in this paper,we propose a novel nonlinear filter for removing“salt and pepper”impulsive noise from a complex color image.The new filter is called the Modified Vector Directional Filter(MVDF).The suggested method is based on the traditional Vector Directional Filter(VDF).However,before the candidate pixel is processed by the VDF,theMVDF employs a threshold and the neighboring pixels of the candidate pixel in a 3×3 filter window to determine whether it is noise-corrupted or noise-free.Several reference color images corrupted by impulsive noise with intensities ranging from 3%to 20%are used to assess theMVDF’s effectiveness.The results of the experiments show that theMVDF is better than the VDF and the Generalized VDF(GVDF)in terms of the PSNR(Peak Signal-to-Noise Ratio),NCD(Normalized Color Difference),and execution time for the denoised image.In fact,the PSNR is increased by 6.554%and 12.624%,the NCD is decreased by 20.273%and 44.147%,and the execution time is reduced by approximately a factor of 3 for the MVDF relative to the VDF and GVDF,respectively.These results prove the efficiency of the proposed filter.Furthermore,a hardware design is proposed for the MVDF using the High-Level Synthesis(HLS)flow in order to increase its performance.This design,which is implemented on the Xilinx ZynqXCZU9EG Field-ProgrammableGate Array(FPGA),allows the restoration of a 256×256-pixel image in 2 milliseconds(ms)only.

Political Optimizer with Deep Learning-Enabled Tongue Color Image Analysis Model

Biomedical image processing is widely utilized for disease detection and classification of biomedical images.Tongue color image analysis is an effective and non-invasive tool for carrying out secondary detection at anytime and anywhere.For removing the qualitative aspect,tongue images are quantitatively inspected,proposing a novel disease classification model in an automated way is preferable.This article introduces a novel political optimizer with deep learning enabled tongue color image analysis(PODL-TCIA)technique.The presented PODL-TCIA model purposes to detect the occurrence of the disease by examining the color of the tongue.To attain this,the PODL-TCIA model initially performs image pre-processing to enhance medical image quality.Followed by,Inception with ResNet-v2 model is employed for feature extraction.Besides,political optimizer(PO)with twin support vector machine(TSVM)model is exploited for image classification process,shows the novelty of the work.The design of PO algorithm assists in the optimal parameter selection of the TSVM model.For ensuring the enhanced outcomes of the PODL-TCIA model,a wide-ranging experimental analysis was applied and the outcomes reported the betterment of the PODL-TCIA model over the recent approaches.

A novel color image encryption algorithm based on genetic recombination and the four-dimensional memristive hyperchaotic system

Recently, many image encryption algorithms based on chaos have been proposed. Most of the previous algorithms encrypt components R, G, and B of color images independently and neglect the high correlation between them. In the paper, a novel color image encryption algorithm is introduced. The 24 bit planes of components R, G, and B of the color plain image are obtained and recombined into 4 compound bit planes, and this can make the three components affect each other. A four-dimensional(4D) memristive hyperchaotic system generates the pseudorandom key streams and its initial values come from the SHA 256 hash value of the color plain image. The compound bit planes and key streams are confused according to the principles of genetic recombination, then confusion and diffusion as a union are applied to the bit planes,and the color cipher image is obtained. Experimental results and security analyses demonstrate that the proposed algorithm is secure and effective so that it may be adopted for secure communication.

Median Filtering Forensics Scheme for Color Images Based on Quaternion Magnitude-Phase CNN

In the paper,a convolutional neural network based on quaternion transformation is proposed to detect median filtering for color images.Compared with conventional convolutional neural network,color images can be processed in a holistic manner in the proposed scheme,which makes full use of the correlation between RGB channels.And due to the use of convolutional neural network,it can effectively avoid the one-sidedness of artificial features.Experimental results have shown the scheme’s improvement over the state-of-the-art scheme on the accuracy of color image median filtering detection.

Color Image Steganalysis Based on Residuals of Channel Differences

This study proposes a color image steganalysis algorithm that extracts highdimensional rich model features from the residuals of channel differences.First,the advantages of features extracted from channel differences are analyzed,and it shown that features extracted in this manner should be able to detect color stego images more effectively.A steganalysis feature extraction method based on channel differences is then proposed,and used to improve two types of typical color image steganalysis features.The improved features are combined with existing color image steganalysis features,and the ensemble classifiers are trained to detect color stego images.The experimental results indicate that,for WOW and S-UNIWARD steganography,the improved features clearly decreased the average test errors of the existing features,and the average test errors of the proposed algorithm is smaller than those of the existing color image steganalysis algorithms.Specifically,when the payload is smaller than 0.2 bpc,the average test error decreases achieve 4%and 3%.

Fusion Based Tongue Color Image Analysis Model for Biomedical Applications

Tongue diagnosis is a novel and non-invasive approach commonly employed to carry out the supplementary diagnosis over the globe.Recently,several deep learning(DL)based tongue color image analysis models have existed in the literature for the effective detection of diseases.This paper presents a fusion of handcrafted with deep features based tongue color image analysis(FHDF-TCIA)technique to biomedical applications.The proposed FDHF-TCIA technique aims to investigate the tongue images using fusion model,and thereby determines the existence of disease.Primarily,the FHDF-TCIA technique comprises Gaussian filtering based preprocessing to eradicate the noise.The proposed FHDF-TCIA model encompasses a fusion of handcrafted local binary patterns(LBP)withMobileNet based deep features for the generation of optimal feature vectors.In addition,the political optimizer based quantum neural network(PO-QNN)based classification technique has been utilized for determining the proper class labels for it.A detailed simulation outcomes analysis of the FHDF-TCIA technique reported the higher accuracy of 0.992.

Non-Associative Algebra Redesigning Block Cipher with Color Image Encryption

The substitution box(S-box)is a fundamentally important component of symmetric key cryptosystem.An S-box is a primary source of non-linearity in modern block ciphers,and it resists the linear attack.Various approaches have been adopted to construct S-boxes.S-boxes are commonly constructed over commutative and associative algebraic structures including Galois fields,unitary commutative rings and cyclic and non-cyclic finite groups.In this paper,first a non-associative ring of order 512 is obtained by using computational techniques,and then by this ring a triplet of 8×8 S-boxes is designed.The motivation behind the designing of these S-boxes is to upsurge the robustness and broaden the key space due to non-associative and noncommutative behavior of the algebraic structure under consideration.A novel color image encryption application is anticipated in which initially these 3 S-boxes are being used to produce confusion in three layers of a standard RGB image.However,for the sake of diffusion 3D Arnold chaotic map is used in the proposed encryption scheme.A comparison with some of existing chaos and S-box dependent color image encryption schemes specs the performance results of the anticipated RGB image encryption and observed as approaching the standard prime level.

Encryption and Decryption of Color Images through Random Disruption of Rows and Columns

In modern society,information is becoming increasingly interconnected through networks,and the rapid development of information technology has caused people to pay more attention to the encryption and the protection of information.Image encryption technology is a key technology for ensuring the security performance of images.We extracted single channel RGB component images from a color image using MATLAB programs,encrypted and decrypted the color images by randomly disrupting rows,columns and regions of the image.Combined with histograms and the visual judgments of encryption images,it is shown that the information of the original image cannot be obtained from the encryption image easily.The results show that the color-image encryptions with the algorithm we used have good effect and fast operation speed.Thus this algorithm has certain practical value.

An Efficient Color-Image Encryption Method Using DNA Sequence and Chaos Cipher

Nowadays,high-resolution images pose several challenges in the context of image encryption.The encryption of huge images’file sizes requires high computational resources.Traditional encryption techniques like,Data Encryption Standard(DES),and Advanced Encryption Standard(AES)are not only inefficient,but also less secure.Due to characteristics of chaos theory,such as periodicity,sensitivity to initial conditions and control parameters,and unpredictability.Hence,the characteristics of deoxyribonucleic acid(DNA),such as vast parallelism and large storage capacity,make it a promising field.This paper presents an efficient color image encryption method utilizing DNA encoding with two types of hyper-chaotic maps.The proposed encryption method comprises three steps.The first step initializes the conditions for generating Lorenz and Rossler hyper-chaotic maps using a plain image Secure Hash Algorithm(SHA-256/384).The second step performs a confusion procedure by scrambling the three components of the image(red,green,and blue)using Lorenz hyper-chaotic sequences.Finally,the third step combines three approaches to encrypt the scrambled components for diffusion:DNA encoding/decoding,addition operation between components,and XORing with Rossler hyper-chaotic sequences.The simulation results indicate that the suggested encryption algorithm satisfies the requirements of security.The entropy value of confusion and diffusion is 7.997,the key space is 2200,and the correlation coefficient is nearly zero.The efficacy of the proposed method has been verified through numerous evaluations,and the results show its resistance and effectiveness against several attacks,like statistical and brute-force attacks.Finally,the devised algorithm vanquishes other relevant color image encryption algorithms.

Saturation-Value Blind Color Image Deblurring with Geometric Spatial-Feature Prior

Blind deblurring for color images has long been a challenging computer vision task.The intrinsic color structures within image channels have typically been disregarded in many excellent works.We investigate employing regularizations in the hue,saturation,and value(HSV)color space via the quaternion framework in order to better retain the internal relationship among the multiple channels and reduce color distortions and color artifacts.We observe that a geometric spatial-feature prior utilized in the intermediate latent image successfully enhances the kernel accuracy for the blind deblurring variational models,preserving the salient edges while decreasing the unfavorable structures.Motivated by this,we develop a saturation-value geometric spatial-feature prior in the HSV color space via the quaternion framework for blind color image deblurring,which facilitates blur kernel estimation.An alternating optimization strategy combined with a primal-dual projected gradient method can effectively solve this novel proposed model.Extensive experimental results show that our model outperforms state-of-the-art methods in blind color image deblurring by a wide margin,demonstrating the effectiveness of the proposed model.

Image Color Rendering Based on Hinge-Cross-Entropy GAN in Internet of Medical Things

Computer-aided diagnosis based on image color rendering promotes medical image analysis and doctor-patient communication by highlighting important information of medical diagnosis.To overcome the limitations of the color rendering method based on deep learning,such as poor model stability,poor rendering quality,fuzzy boundaries and crossed color boundaries,we propose a novel hinge-cross-entropy generative adversarial network(HCEGAN).The self-attention mechanism was added and improved to focus on the important information of the image.And the hinge-cross-entropy loss function was used to stabilize the training process of GAN models.In this study,we implement the HCEGAN model for image color rendering based on DIV2K and COCO datasets,and evaluate the results using SSIM and PSNR.The experimental results show that the proposed HCEGAN automatically re-renders images,significantly improves the quality of color rendering and greatly improves the stability of prior GAN models.

Symbolic Images of Colors, Animals and Machines in The Red Badge of Courage

Abstract:Stephen Crane was an outstanding American novelist,poet,and journalist.He achieved great success in his literary works during his brief career.Crane’s most well-known work,The Red Badge of Courage,is commonly believed to be the first great novel of the American Civil War,largely because of its vivid and detailed description of the experience of warfare.This paper analyzes the images of color,animal and machine,which convey Crane’s thoughts of war:war is full of chaos,brutality,and confusion,without any romantic elements or heroism.

A novel color image encryption algorithm based on a fractional-order discrete chaotic neural network and DNA sequence operations

A novel color image encryption algorithm based on dynamic deoxyribonucleic acid(DNA)encoding and chaos is presented.A three-neuron fractional-order discrete Hopfield neural network(FODHNN)is employed as a pseudo-random chaotic sequence generator.Its initial value is obtained with the secret key generated by a fiveparameter external key and a hash code of the plain image.The external key includes both the FODHNN discrete step size and order.The hash is computed with the SHA-2 function.This ensures a large secret key space and improves the algorithm sensitivity to the plain image.Furthermore,a new three-dimensional projection confusion method is proposed to scramble the pixels among red,green,and blue color components.DNA encoding and diffusion are used to diffuse the image information.Pseudo-random sequences generated by FODHNN are employed to determine the encoding rules for each pixel and to ensure the diversity of the encoding methods.Finally,confusion II and XOR are used to ensure the security of the encryption.Experimental results and the security analysis show that the proposed algorithm has better performance than those reported in the literature and can resist typical attacks.

Low and non-uniform illumination color image enhancement using weighted guided image filtering

In the state of the art,grayscale image enhancement algorithms are typically adopted for enhancement of RGB color images captured with low or non-uniform illumination.As these methods are applied to each RGB channel independently,imbalanced inter-channel enhancements(color distortion)can often be observed in the resulting images.On the other hand,images with non-uniform illumination enhanced by the retinex algorithm are prone to artifacts such as local blurring,halos,and over-enhancement.To address these problems,an improved RGB color image enhancement method is proposed for images captured under nonuniform illumination or in poor visibility,based on weighted guided image filtering(WGIF).Unlike the conventional retinex algorithm and its variants,WGIF uses a surround function instead of a Gaussian filter to estimate the illumination component;it avoids local blurring and halo artifacts due to its anisotropy and adaptive local regularization.To limit color distortion,RGB images are first converted to HSI(hue,saturation,intensity)color space,where only the intensity channel is enhanced,before being converted back to RGB space by a linear color restoration algorithm.Experimental results show that the proposed method is effective for both RGB color and grayscale images captured under low exposure and non-uniform illumination,with better visual quality and objective evaluation scores than from comparator algorithms.It is also efficient due to use of a linear color restoration algorithm.

Studying pressure sores through illuminant invariant assessment of digital color images

Methods for pressure sore monitoring remain both a clinical and research challenge.Improved methodologies could assist physicians in developing prompt and effective pressure sore interventions.In this paper a technique is introduced for the assessment of pressure sores in guinea pigs,using captured color images.Sores were artificially induced,utilizing a system particularly developed for this purpose.Digital images were obtained from the suspicious region in days 3 and 7 post-pressure sore generation.Different segments of the color images were divided and labeled into three classes,based on their severity status.For quantitative analysis,a color based texture model,which is invariant against monotonic changes in illumination,is proposed.The texture model has been developed based on the local binary pattern operator.Tissue segments were classified,using the texture model and its features as inputs to a combination of neural networks.Our method is capable of discriminating tissue segments in different stages of pressure sore generation,and therefore can be a feasible tool for the early assessment of pressure sores.