Image Splicing Forgery Detection Using Feature-Based of Sonine Functions and Deep Features

The growing prevalence of fake images on the Internet and social media makes image integrity verification a crucial research topic.One of the most popular methods for manipulating digital images is image splicing,which involves copying a specific area from one image and pasting it into another.Attempts were made to mitigate the effects of image splicing,which continues to be a significant research challenge.This study proposes a new splicing detectionmodel,combining Sonine functions-derived convex-based features and deep features.Two stages make up the proposed method.The first step entails feature extraction,then classification using the“support vector machine”(SVM)to differentiate authentic and spliced images.The proposed Sonine functions-based feature extraction model reveals the spliced texture details by extracting some clues about the probability of image pixels.The proposed model achieved an accuracy of 98.93% when tested with the CASIA V2.0 dataset“Chinese Academy of Sciences,Institute of Automation”which is a publicly available dataset for forgery classification.The experimental results show that,for image splicing forgery detection,the proposed Sonine functions-derived convex-based features and deep features outperform state-of-the-art techniques in terms of accuracy,precision,and recall.Overall,the obtained detection accuracy attests to the benefit of using the Sonine functions alongside deep feature representations.Finding the regions or locations where image tampering has taken place is limited by the study.Future research will need to look into advanced image analysis techniques that can offer a higher degree of accuracy in identifying and localizing tampering regions.

Image Splicing Detection Using Generalized Whittaker Function Descriptor

Image forgery is a crucial part of the transmission of misinformation,which may be illegal in some jurisdictions.The powerful image editing software has made it nearly impossible to detect altered images with the naked eye.Images must be protected against attempts to manipulate them.Image authentication methods have gained popularity because of their use in multimedia and multimedia networking applications.Attempts were made to address the consequences of image forgeries by creating algorithms for identifying altered images.Because image tampering detection targets processing techniques such as object removal or addition,identifying altered images remains a major challenge in research.In this study,a novel image texture feature extraction model based on the generalized k-symbolWhittaker function(GKSWF)is proposed for better image forgery detection.The proposed method is divided into two stages.The first stage involves feature extraction using the proposed GKSWF model,followed by classification using the“support vector machine”(SVM)to distinguish between authentic and manipulated images.Each extracted feature from an input image is saved in the features database for use in image splicing detection.The proposed GKSWF as a feature extraction model is intended to extract clues of tampering texture details based on the probability of image pixel.When tested on publicly available image dataset“CASIA”v2.0(ChineseAcademy of Sciences,Institute of Automation),the proposed model had a 98.60%accuracy rate on the YCbCr(luminance(Y),chroma blue(Cb)and chroma red(Cr))color spaces in image block size of 8×8 pixels.The proposed image authentication model shows great accuracy with a relatively modest dimension feature size,supporting the benefit of utilizing the k-symbol Whittaker function in image authentication algorithms.

Pixel’s Quantum Image Enhancement Using Quantum Calculus

The current study provides a quantum calculus-based medical image enhancement technique that dynamically chooses the spatial distribution of image pixel intensity values.The technique focuses on boosting the edges and texture of an image while leaving the smooth areas alone.The brain Magnetic Resonance Imaging(MRI)scans are used to visualize the tumors that have spread throughout the brain in order to gain a better understanding of the stage of brain cancer.Accurately detecting brain cancer is a complex challenge that the medical system faces when diagnosing the disease.To solve this issue,this research offers a quantum calculus-based MRI image enhancement as a pre-processing step for brain cancer diagnosis.The proposed image enhancement approach improves images with low gray level changes by estimating the pixel’s quantum probability.The suggested image enhancement technique is demonstrated to be robust and resistant to major quality changes on a variety ofMRIscan datasets of variable quality.ForMRI scans,the BRISQUE“blind/referenceless image spatial quality evaluator”and the NIQE“natural image quality evaluator”measures were 39.38 and 3.58,respectively.The proposed image enhancement model,according to the data,produces the best image quality ratings,and it may be able to aid medical experts in the diagnosis process.The experimental results were achieved using a publicly available collection of MRI scans.

An overview of intelligent selection and prediction method in heterogeneous wireless networks

Heterogeneous wireless access technologies will coexist in next generation wireless networks.These technologies form integrated networks,and these networks support multiple services with high quality level.Various access technologies allow users to select the best available access network to meet the requirements of each type of communication service.Being always best connected anytime and anywhere is a major concern in a heterogeneous wireless networks environment.Always best connected enables network selection mechanisms to keep mobile users always connected to the best network.We present an overview of the network selection and prediction problems and challenges.In addition,we discuss a comprehensive classification of related theoretic approaches,and also study the integration between these methods,finding the best solution of network selection and prediction problems.The optimal solution can fulfill the requirements of the next generation wireless networks.

A New Medical Image Enhancement Algorithm Based on Fractional Calculus

The enhancement of medical images is a challenging research task due to the unforeseeable variation in the quality of the captured images.The captured images may present with low contrast and low visibility,which might inuence the accuracy of the diagnosis process.To overcome this problem,this paper presents a new fractional integral entropy(FITE)that estimates the unforeseeable probabilities of image pixels,posing as the main contribution of the paper.The proposed model dynamically enhances the image based on the image contents.The main advantage of FITE lies in its capability to enhance the low contrast intensities through pixels’probability.Initially,the pixel probability of the fractional power is utilized to extract the illumination value from the pixels of the image.Next,the contrast of the image is then adjusted to enhance the regions with low visibility.Finally,the fractional integral entropy approach is implemented to enhance the low visibility contents from the input image.Tests were conducted on brain MRI,lungs CT,and kidney MRI scans datasets of different image qualities to show that the proposed model is robust and can withstand dramatic variations in quality.The obtained comparative results show that the proposed image enhancement model achieves the best BRISQUE and NIQE scores.Overall,this model improves the details of brain MRI,lungs CT,and kidney MRI scans,and could therefore potentially help the medical staff during the diagnosis process.

Fractional Rényi Entropy Image Enhancement for Deep Segmentation of Kidney MRI

Recently,many rapid developments in digital medical imaging have made further contributions to health care systems.The segmentation of regions of interest in medical images plays a vital role in assisting doctors with their medical diagnoses.Many factors like image contrast and quality affect the result of image segmentation.Due to that,image contrast remains a challenging problem for image segmentation.This study presents a new image enhancement model based on fractional Rényi entropy for the segmentation of kidney MRI scans.The proposed work consists of two stages:enhancement by fractional Rényi entropy,and MRI Kidney deep segmentation.The proposed enhancement model exploits the pixel’s probability representations for image enhancement.Since fractional Rényi entropy involves fractional calculus that has the ability to model the non-linear complexity problem to preserve the spatial relationship between pixels,yielding an overall better details of the kidney MRI scans.In the second stage,the deep learning kidney segmentation model is designed to segment kidney regions in MRI scans.The experimental results showed an average of 95.60%dice similarity index coefficient,which indicates best overlap between the segmented bodies with the ground truth.It is therefore concluded that the proposed enhancement model is suitable and effective for improving the kidney segmentation performance.

Continuous solutions for fractional integral inclusion in locally convex topological space

The existence of continuous solutions for fractional integral inclusion via its singlevalued problem and fixed point theorem for set-valued function in locally convex topological spaces is discussed. The proof of the single-valued problem will be based on the Leray- Schauder fixed point theorem. Moreover, the controllability of this solution is studied.

ON A NEW CLASS OF ANALYTIC FUNCTION DERIVED BY A FRACTIONAL DIFFERENTIAL OPERATOR

Making use of the fractional differential operator, we impose and study a new class of analytic functions in the unit disk （type fractional differential equation）. The main object of this paper is to investigate inclusion relations, coefficient bound for this class. Moreover, we discuss some geometric properties of the fractional differential operator.

Similarity Analytic Solutions of a 3D-Fractal Nanofluid Uncoupled System Optimized by a Fractal Symmetric Tangent Function

The science of strategy(game theory)is known as the optimal decision-making of autonomous and challenging players in a strategic background.There are different strategies to complete the optimal decision.One of these strategies is the similarity technique.Similarity technique is a generalization of the symmetric strategy,which depends only on the other approaches employed,which can be formulated by altering diversities.One of these methods is the fractal theory.In this investigation,we present a new method studying the similarity analytic solution(SAS)of a 3D-fractal nanofluid system(FNFS).The dynamic evolution is completely given by the concept of differential subordination and majorization.Subordination andmajorization relationships are the sets of observable individualities.Game theory can simplify the conditions under which particular sets combine.We offer an explicit construction for the complex possible velocity,energy and thermal functions of two-dimensional fluid flow(the complex variable is suggested in the open unit disk,where the disk is selected at a constant temperature and concentration with uniform velocity).We establish that whenever the 3D-fractal nanofluid systemis approximated by a fractal function,the solution has the same property,so a class of fractal tangent function gives SAS.Finally,we demonstrate some simulations and examples that give the consequences of this methodology.

Image Splicing Detection Based on Texture Features with Fractal Entropy

Over the past years,image manipulation tools have become widely accessible and easier to use,which made the issue of image tampering far more severe.As a direct result to the development of sophisticated image-editing applications,it has become near impossible to recognize tampered images with naked eyes.Thus,to overcome this issue,computer techniques and algorithms have been developed to help with the identification of tampered images.Research on detection of tampered images still carries great challenges.In the present study,we particularly focus on image splicing forgery,a type of manipulation where a region of an image is transposed onto another image.The proposed study consists of four features extraction stages used to extract the important features from suspicious images,namely,Fractal Entropy(FrEp),local binary patterns(LBP),Skewness,and Kurtosis.The main advantage of FrEp is the ability to extract the texture information contained in the input image.Finally,the“support vector machine”(SVM)classification is used to classify images into either spliced or authentic.Comparative analysis shows that the proposed algorithm performs better than recent state-of-the-art of splicing detection methods.Overall,the proposed algorithm achieves an ideal balance between performance,accuracy,and efficacy,which makes it suitable for real-world applications.

Image Encryption Algorithm Based on New Fractional Beta Chaotic Maps

In this study,a new algorithm of fractional beta chaotic maps is proposed to generate chaotic sequences for image encryption.The proposed technique generates multi random sequences by shuffling the image pixel position.This technique is used to blur the pixels connecting the input and encrypted images and to increase the attack resistance.The proposed algorithm makes the encryption process sophisticated by using fractional chaotic maps,which hold the properties of pseudo-randomness.The fractional beta sequences are utilized to alter the image pixels to decryption attacks.The experimental results proved that the proposed image encryption algorithm successfully encrypted and decrypted the images with the same keys.The output findings indicate that our proposed algorithm has good entropy and low correlation coefficients.This translates to enhanced security against different attacks.A MATLAB programming tool was used to implement and assess the image quality measures.A comparison with other image encryption techniques regarding the visual inspection and signal-to-noise ratio is provided.

Analytic solutions of the generalized water wave dynamical equations based on time-space symmetric differential operator

It is well known that there is a deep connection between the symmetric and traveling wave solutions.It has been shown that all symmetric waves are traveling waves.In this paper,we establish new analytic solution collections of nonlinear conformable time-fractional water wave dynamical equation in a complex domain.For this purpose,we construct a new definition of a symmetric conformable differential operator(SCDO).The operator has a symmetric representation in the open unit disk.By using SCDO,we generalize a class of water wave dynamical equation type time-space fractional complex Ginzburg-Landau equation.The results show that the obtainable approaches are powerful,dependable and prepared to apply to all classes of complex differential equations.