A Spatiotemporal-chaos-based Encryption Having Overall Properties Considerably Better Than Advanced Encryption Standard

Spatiotemporal chaos of a two-dimensional one-way coupled map lattice is used for chaotic cryptography. The chaotic outputs of many space units are used for encryption simultaneously. This system shows satisfactory cryptographic properties of high security; fast encryption (decryption) speed; and robustness against noise disturbances in communication channel. The overall features of this spatiotemporal-chaos-based cryptosystem are better than chaotic cryptosystems known so far, and also than currently used conventional cryptosystems, such as the Advanced Encryption Standard (AES).Comment: 11 pages, 3 figure

The Origins of Computational Mechanics: A Brief Intellectual History and Several Clarifications

The principle goal of computational mechanics is to define pattern and structure so that the organization of complex systems can be detected and quantified. Computational mechanics developed from efforts in the 1970s and early 1980s to identify strange attractors as the mechanism driving weak fluid turbulence via the method of reconstructing attractor geometry from measurement time series and in the mid-1980s to estimate equations of motion directly from complex time series. In providing a mathematical and operational definition of structure it addressed weaknesses of these early approaches to discovering patterns in natural systems. Since then, computational mechanics has led to a range of results from theoretical physics and nonlinear mathematics to diverse applications---from closed-form analysis of Markov and non-Markov stochastic processes that are ergodic or nonergodic and their measures of information and intrinsic computation to complex materials and deterministic chaos and intelligence in Maxwellian demons to quantum compression of classical processes and the evolution of computation and language. This brief review clarifies several misunderstandings and addresses concerns recently raised regarding early works in the field (1980s). We show that misguided evaluations of the contributions of computational mechanics are groundless and stem from a lack of familiarity with its basic goals and from a failure to consider its historical context. For all practical purposes, its modern methods and results largely supersede the early works. This not only renders recent criticism moot and shows the solid ground on which computational mechanics stands but, most importantly, shows the significant progress achieved over three decades and points to the many intriguing and outstanding challenges in understanding the computational nature of complex dynamic systems.Comment: 11 pages, 123 citations; http://csc.ucdavis.edu/~cmg/compmech/pubs/cmr.ht

Inverting Chaos: Extracting System Parameters from Experimental Data

Given a set of experimental or numerical chaotic data and a set of model differential equations with several parameters, is it possible to determine the numerical values for these parameters using a least-squares approach, and thereby to test the model against the data? We explore this question (a) with simulated data from model equations for the Rossler, Lorenz, and pendulum attractors, and (b) with experimental data produced by a physical chaotic pendulum. For the systems considered in this paper, the least-squares approach provides values of model parameters that agree well with values obtained in other ways, even in the presence of modest amounts of added noise. For experimental data, the “fitted” and experimental attractors are found to have the same correlation dimension and the same positive Lyapunov exponent

A Novel Diffusion-Permutation Image Encryption Scheme Based on Spatiotemporal Chaos

The spatiotemporal chaos possesses better properties than simple chaotic system, which has attracted more and more attention by the researchers in the image encryption field. This paper presents a novel image encryption scheme based on spatiotemporal chaos. The algorithm uses the spatiotemporal chaos to diffuse plain image and an Arnold map shuffle the positions of pixels. Test results and security analysis not only show that the scheme is characteristic of excellent sensitivity to the original image and keys, large secret key space and high expansibility, but also has excellent effective encryption and strong anti-attacking performance

A New Color Image Encryption Scheme Using CML and a Fractional-Order Chaotic System

Funding: This research was jointly supported by the National Natural Science Foundation of China (No. 61004006, http://www.nsfc.gov.cn), China Postdoctoral Science Foundation(No. 2013M530181, http://res.chinapostdoctor.org.cn/BshWeb/index.shtml), the Natural Science Foundation of Henan Province, China (No. 13230010254, http://www.hnkjt.gov.cn/, Program for Science & Technology Innovation Talents in Universities of Henan Province, China (Grant No 14HASTIT042, http://rcloud.edu.cn), the Foundation for University Young Key Teacher Program of Henan Province, China (No. 2011GGJS-025, http://www.haedu.gov.cn/), Shanghai Postdoctoral Scientific Program (No. 13R21410600, http://www.21cnhr.gov.cn/doctorarea/), the Science & Technology Project Plan of Archives Bureau of Henan Province (No. 2012-X-62, http://www.hada.gov.cn/) and the Natural Science Foundation of Educational Committee of Henan Province, China (No. 13A520082, http://www.haedu.gov.cn/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

Invariant template matching in systems with spatiotemporal coding: a vote for instability

We consider the design of a pattern recognition that matches templates to images, both of which are spatially sampled and encoded as temporal sequences. The image is subject to a combination of various perturbations. These include ones that can be modeled as parameterized uncertainties such as image blur, luminance, translation, and rotation as well as unmodeled ones. Biological and neural systems require that these perturbations be processed through a minimal number of channels by simple adaptation mechanisms. We found that the most suitable mathematical framework to meet this requirement is that of weakly attracting sets. This framework provides us with a normative and unifying solution to the pattern recognition problem. We analyze the consequences of its explicit implementation in neural systems. Several properties inherent to the systems designed in accordance with our normative mathematical argument coincide with known empirical facts. This is illustrated in mental rotation, visual search and blur/intensity adaptation. We demonstrate how our results can be applied to a range of practical problems in template matching and pattern recognition.Comment: 52 pages, 12 figure

COMPARATIVE STUDY OF CHAOTIC SYSTEM FOR ENCRYPTION

Chaotic systems leverage their inherent complexity and unpredictability to generate cryptographic keys, enhancing the security of encryption algorithms. This paper presents a comparative study of 13 chaotic keymaps. Several evaluation metrics, including keyspace size, dimensions, entropy, statistical properties, sensitivity to initial conditions, security level, practical implementation, and adaptability to cloud computing, are utilized to compare the keymaps. Keymaps such as Logistic, Lorenz, and Henon demonstrate robustness and high-security levels, offering large key space sizes and resistance to attacks. Their efficient implementation in a cloud computing environment further validates their suitability for real-world encryption scenarios. The context of the study focuses on the role of the key in encryption and provides a brief specification of each map to assess the effectiveness, security, and suitability of the popular chaotic keymaps for encryption applications. The study also discusses the security assessment of resistance to the popular cryptographic attacks: brute force, known plaintext, chosen plaintext, and side channel. The findings of this comparison reveal the Lorenz Map is the best for the cloud environment based on a specific scenario