Asymmetric image encryption algorithm based on a new three-dimensional improved logistic chaotic map

Based on some analyses of existing chaotic image encryption frameworks and a new designed three-dimensional improved logistic chaotic map(3D-ILM),an asymmetric image encryption algorithm using public-key Rivest–Shamir–Adleman(RSA)is presented in this paper.In the first stage,a new 3D-ILM is proposed to enhance the chaotic behavior considering analysis of time sequence,Lyapunov exponent,and Shannon entropy.In the second stage,combined with the public key RSA algorithm,a new key acquisition mathematical model(MKA)is constructed to obtain the initial keys for the 3D-ILM.Consequently,the key stream can be produced depending on the plain image for a higher security.Moreover,a novel process model(NPM)for the input of the 3D-ILM is built,which is built to improve the distribution uniformity of the chaotic sequence.In the third stage,to encrypt the plain image,a pre-process by exclusive OR(XOR)operation with a random matrix is applied.Then,the pre-processed image is performed by a permutation for rows,a downward modulo function for adjacent pixels,a permutation for columns,a forward direction XOR addition-modulo diffusion,and a backward direction XOR addition-modulo diffusion to achieve the final cipher image.Moreover,experiments show that the the proposed algorithm has a better performance.Especially,the number of pixels change rate(NPCR)is close to ideal case 99.6094%,with the unified average changing intensity(UACI)close to 33.4634%,and the information entropy(IE)close to 8.

Image encryption based on a delayed fractional-order chaotic logistic system

A new image encryption scheme is proposed based on a delayed fractional-order chaotic logistic system.In the process of generating a key stream,the time-varying delay and fractional derivative are embedded in the proposed scheme to improve the security.Such a scheme is described in detail with security analyses including correlation analysis,information entropy analysis,run statistic analysis,mean-variance gray value analysis,and key sensitivity analysis.Experimental results show that the newly proposed image encryption scheme possesses high security.

Prediction on Failure Pressure of Pipeline Containing Corrosion Defects Based on ISSA-BPNNModel

Oil and gas pipelines are affected by many factors,such as pipe wall thinning and pipeline rupture.Accurate prediction of failure pressure of oil and gas pipelines can provide technical support for pipeline safety management.Aiming at the shortcomings of the BP Neural Network(BPNN)model,such as low learning efficiency,sensitivity to initial weights,and easy falling into a local optimal state,an Improved Sparrow Search Algorithm(ISSA)is adopted to optimize the initial weights and thresholds of BPNN,and an ISSA-BPNN failure pressure prediction model for corroded pipelines is established.Taking 61 sets of pipelines blasting test data as an example,the prediction model was built and predicted by MATLAB software,and compared with the BPNN model,GA-BPNN model,and SSA-BPNN model.The results show that the MAPE of the ISSA-BPNN model is 3.4177%,and the R2 is 0.9880,both of which are superior to its comparison model.Using the ISSA-BPNN model has high prediction accuracy and stability,and can provide support for pipeline inspection and maintenance.

Fuzzy Hybrid Coyote Optimization Algorithm for Image Thresholding

In order to address the problems of Coyote Optimization Algorithm in image thresholding,such as easily falling into local optimum,and slow convergence speed,a Fuzzy Hybrid Coyote Optimization Algorithm(here-inafter referred to as FHCOA)based on chaotic initialization and reverse learning strategy is proposed,and its effect on image thresholding is verified.Through chaotic initialization,the random number initialization mode in the standard coyote optimization algorithm(COA)is replaced by chaotic sequence.Such sequence is nonlinear and long-term unpredictable,these characteristics can effectively improve the diversity of the population in the optimization algorithm.Therefore,in this paper we first perform chaotic initialization,using chaotic sequence to replace random number initialization in standard COA.By combining the lens imaging reverse learning strategy and the optimal worst reverse learning strategy,a hybrid reverse learning strategy is then formed.In the process of algorithm traversal,the best coyote and the worst coyote in the pack are selected for reverse learning operation respectively,which prevents the algorithm falling into local optimum to a certain extent and also solves the problem of premature convergence.Based on the above improvements,the coyote optimization algorithm has better global convergence and computational robustness.The simulation results show that the algorithmhas better thresholding effect than the five commonly used optimization algorithms in image thresholding when multiple images are selected and different threshold numbers are set.

Securing color image transmission using compression-encryption model with dynamic key generator and efficient symmetric key distribution

There are a few issues related to the existing symmetric encryption models for color image data,such as the key generation and distribution problems.In this paper,we propose a compression-encryption model to solve these problems.This model consists of three processes.The first process is the dynamic symmetric key generation.The second one is the compression process,which is followed by encryption using keystreams and S-Boxes that are generated using a chaotic logistic map.The last process is the symmetric key distribution.The symmetric key is encrypted twice using Rivest-Shamir-Adleman(RSA)to provide both authentication and confidentiality.Then,it is inserted into the cipher image using the End of File(EoF)method.The evaluation shows that the symmetric key generator model can produce a random and dynamic symmetric key.Hence,the image data is safe from ciphertext-only attacks.This model is fast and able to withstand entropy attacks,statistical attacks,differential attacks,and brute-force attacks.

A novel hybrid cryptosystem based on DQFrFT watermarking and 3D-CLM encryption for healthcare services

Quaternion algebra has been used to apply the fractional Fourier transform(FrFT)to color images in a comprehensive approach.However,the discrete fractional random transform(DFRNT)with adequate basic randomness remains to be examined.This paper presents a novel multistage privacy system for color medical images based on discrete quaternion fractional Fourier transform(DQFrFT)watermarking and three-dimensional chaotic logistic map(3D-CLM)encryption.First,we describe quaternion DFRNT(QDFRNT),which generalizes DFRNT to handle quaternion signals effectively,and then use QDFRNT to perform color medical image adaptive watermarking.To efficiently evaluate QDFRNT,this study derives the relationship between the QDFRNT of a quaternion signal and the four components of the DFRNT signal.Moreover,it uses the human vision system's(HVS)masking qualities of edge,texture,and color tone immediately from the color host image to adaptively modify the watermark strength for each block in the color medical image using the QDFRNT-based adaptive watermarking and support vector machine(SVM)techniques.The limitations of watermark embedding are also explained to conserve watermarking energy.Second,3D-CLM encryption is employed to improve the system's security and efficiency,allowing it to be used as a multistage privacy system.The proposed security system is effective against many types of channel noise attacks,according to simulation results.

Image Encryption Application of Chaotic Sequences Incorporating Quantum Keys

This paper proposes an image encryption algorithm LQBPNN（logistic quantum and back propagation neural network）based on chaotic sequences incorporating quantum keys. Firstly, the improved one-dimensional logistic chaotic sequence is used as the basic key sequence. After the quantum key is introduced, the quantum key is incorporated into the chaotic sequence by nonlinear operation. Then the pixel confused process is completed by the neural network. Finally, two sets of different mixed secret key sequences are used to perform two rounds of diffusion encryption on the confusing image. The experimental results show that the randomness and uniformity of the key sequence are effectively enhanced. The algorithm has a secret key space greater than 2182. The adjacent pixel correlation of the encrypted image is close to 0, and the information entropy is close to 8. The ciphertext image can resist several common attacks such as typical attacks, statistical analysis attacks and differential attacks.