Informatica

This paper proposes a new family of 4-dimensional chaotic cat maps. This family is then used in the design of a novel block-based image encryption scheme. This scheme is composed of two independent phases, a robust light shuffling phase and a masking phase which operate on image-blocks. It utilizes measures of central tendency to mix blocks of the image at hand to enhance security against a number of cryptanalytic attacks. The mixing is designed so that while encryption is highly sensitive to the secret key and the input image, decryption is robust against noise and cropping of the cipher-image. Empirical results show high performance of the suggested scheme and its robustness against well-known cryptanalytic attacks. Furthermore, comparisons with existing image encryption methods are presented which demonstrate the superiority of the proposed scheme.

A $(k,n)$-threshold secret image sharing scheme is any method of distributing a secret image amongst n participants in such a way that any k participants are able to use their shares collectively to reconstruct the secret image, while fewer than k shares do not reveal any information about the secret image. In this work, we propose a lossless linear algebraic $(k,n)$-threshold secret image sharing scheme. The scheme associates a vector ${\mathbf{v}_{i}}$ to the ith participant in the vector space ${\mathbb{F}_{{2^{\alpha }}}^{k}}$, where the vectors ${\mathbf{v}_{i}}$ satisfy some admissibility conditions. The ith share is simply a linear combination of the vectors ${\mathbf{v}_{i}}$ with coefficients from the secret image. Simulation results demonstrate the effectiveness and robustness of the proposed scheme compared to standard statistical attacks on secret image sharing schemes. Furthermore, the proposed scheme has a high level of security, error-resilient capability, and the size of each share is $1/k$ the size of the secret image. In comparison with existing work, the scheme is shown to be very competitive.

Scalar quantizer selection for processing a signal with a unit variance is a difficult problem, while both selection and quantizer design for the range of variances is even tougher and to the authors’ best knowledge, it is not theoretically solved. Furthermore, performance estimation of various image processing algorithms is unjustifiably neglected and there are only a few analytical models that follow experimental analysis. In this paper, we analyse application of piecewise uniform quantizer with Golomb-Rice coding in modified block truncation coding algorithm for grayscale image compression, propose design improvements and provide a novel analytical model for performance analysis. Besides the nature of input signal, required compression rate and processing delay of the observed system have a strong influence on quantizer design. Consequently, the impact of quantizer range choice is analysed using a discrete designing variance and it was exploited to improve overall quantizer performance, whereas variable-length coding is applied in order to reduce quantizer’s fixed bit-rate. The analytical model for performance analysis is proposed by introducing Inverse Gaussian distribution and it is obtained by discussing a number of images, providing general closed-form solutions for peak-signal-to-noise ratio and the total average bit-rate estimation. The proposed quantizer design ensures better performance in comparison to the other similar methods for grayscale image compression, including linear prediction of pixel intensity and edge-based adaptation, whereas analytical model for performance analysis provides matching with the experimental results within the range of 1 dB for PSQNR and 0.2 bpp for the total average bit-rate.

We consider a generalized model of neural network with a fuzziness and chaos. The origin of the fuzzy signals lies in complex biochemical and electrical processes of the synapse and dendrite membrane excitation and the inhibition mechanism. The mathematical operations included into fuzzy neural network modeling are: the scalar product between inputs of layers and synaptic weights is replaced by a fuzzy logic multiplication, the sum of products changes to the fuzzy logic sums, and the operators such as supremum, maximum, and minimum are presented for a fuzzy description. The algorithm of varying membership functions, built basing on a backpropagation paradigm and a method of fuzzy neural optimization, has been considered. Both fuzzy properties and a chaos phenomenon are analyzed basing upon experimental computations.