IoT-Based Multi-Dimensional Chaos Mapping System for Secure and Fast Transmission of Visual Data in Smart Cities

A “smart city” sends data from many sensors to a cloud server for local authorities and the public to connect. Smart city residents communicate mostly through images and videos. Many image security algorithms have been proposed to improve locals’ lives, but a high-class redundancy method with a small space requirement is still needed to acquire and protect this sensitive data. This paper proposes an IoT-based multi-dimensional chaos mapping system for secure and fast transmission of visual data in smart cities, which uses the five dimensional Gauss Sine Logistic system to generate hyper-chaotic sequences to encrypt images. The proposed method also uses pixel position permutation and Singular Value Decomposition with Discrete fractional cosine transform to compress and protect the sensitive image data. To increase security, we use a chaotic system to construct the chaotic sequences and a diffusion matrix. Furthermore, numerical simulation results and theoretical evaluations validate the suggested scheme’s security and efficacy after compression encryption.publishedVersio

A Review on Encryption and Decryption of Image using Canonical Transforms & Scrambling Technique

Data security is a prime objective of various researchers & organizations. Because we have to send the data from one end to another end so it is very much important for the sender that the information will reach to the authorized receiver & with minimum loss in the original data. Data security is required in various fields like banking, defence, medical etc. So our objective here is that how to secure the data. So for this purpose we have to use encryption schemes. Encryption is basically used to secure the data or information which we have to transmit or to store. Various methods for the encryption are provided by various researchers. Some of the methods are based on the random keys & some are based on the scrambling scheme. Chaotic map, logistic map, Fourier transform & Fractional Fourier transform etc. are widely used for the encryption process. Now day’s image encryption method is very popular for the encryption scheme. The information is encrypted in the form of image. The encryption is done in a format so no one can read that image. Only the person who are authenticated or have authentication keys can only read that data or information. So this work is based on the same fundamental concept. Here we use Linear Canonical Transform for the encryption process

Noise-Crypt: Image Encryption with Non-linear Noise, Hybrid Chaotic Maps, and Hashing

To secure the digital images over insecure transmission channels, a new image encryption algorithm Noise-Crypt is proposed in this paper. Noise-Crypt integrates non-linear random noise, hybrid chaotic maps, and SHA-256 hashing algorithm. The utilized hybrid chaotic maps are the logistic-tent and the logistic-sine-cosine map. The hybrid chaotic maps enhance the pseudorandom sequence generation and selection of substitution boxes, while the logistic-sine-cosine map induces non-linearity in the algorithm through random noise. This deliberate inclusion of noise contributes to increased resistance against cryptanalysis. The proposed scheme has been evaluated for several security parameters, such as differential attacks, entropy, correlation, etc. Extensive evaluation demonstrates the efficacy of the proposed scheme, with almost ideal values of entropy of 7.99 and correlation of -0.0040. Results of the security analysis validate the potency of the proposed scheme in achieving robust image encryption

Entropy in Image Analysis II

Image analysis is a fundamental task for any application where extracting information from images is required. The analysis requires highly sophisticated numerical and analytical methods, particularly for those applications in medicine, security, and other fields where the results of the processing consist of data of vital importance. This fact is evident from all the articles composing the Special Issue "Entropy in Image Analysis II", in which the authors used widely tested methods to verify their results. In the process of reading the present volume, the reader will appreciate the richness of their methods and applications, in particular for medical imaging and image security, and a remarkable cross-fertilization among the proposed research areas