3D Non-degenerate Hyperchaos: Design, Analysis, and Application in Image Encryption

The Internet of things is a new technological revolution following the computer and Internet. It aims to connect all physical objects existing in the world and forms a network with everything. In recent years, smart home gradually enters into our life. Smart home uses the Internet of things technology to connect all kinds of devices in the home, to achieve a smart home environment. Although the development of smart home has brought a qualitative leap to people’s life, there are many problems in security. Privacy security is one of the challenges to the smart home environment. Attackers can intrude various smart devices in the smart home environment, to achieve the purpose of stealing users’ personal information and privacy. Among these devices, smart cameras are the most intruded frequently. Since many cameras are installed in users’ homes to achieve real-time monitoring of the environment, but the existence of these cameras provides a channel to get information for attackers. In recent years, the leak of video privacy is emerging in an endless stream. According to the researches about privacy protection, this paper proposes a new scheme to selectively encrypt the video captured by the cameras through machine learning technology, so as to protect the personal privacy of users and improve the security of the smart home environment.

Nonlinear analysis, circuit implementation, and application in image encryption of a four-dimensional multi-scroll hyper-chaotic system

A new method of color image encryption is proposed in this paper, with the combination of a two-dimensional (2D) Henon-like chaotic map and compressed sensing for better performance. A sinusoidal function is introduced into the 2D Henon map for increasing randomness. Image encryption algorithm includes three procedures: compression of a color image through the Bernoulli observation matrix, encryption of the compressed image by 2D-Henon map with sinusoidal factor, and reconstruction of color image based on compressed sensing algorithm of orthogonal matching pursuit (OMP). Numerical simulation shows that the proposed encryption algorithm has some advantages including being hardware-friendly, sufficient keyspace, and higher security.

SummaryIn this paper, a new fifth‐order eight‐wing chaotic system is proposed on the basis of a five‐dimensional four‐wing chaotic system. The resulting new system is used as a basis for introducing a method that can control both the number of wings of the system and the direction of the change in the number of wings. Add a rounding function with a cosine function or a sine function to the system equation. The number of wings of the control system is controlled by changing the parameter values of the rounding function. This method can significantly increase the complexity of the system when applied in multiple directions of the system equations. And this method can achieve the purpose of controlling the wing number of the whole system by changing only one parameter. By observing the phase diagram and bifurcation diagram of the system, it is found that it has more complex chaotic properties and rich dynamical behavior and there are coexisting attractors. The chaotic properties of the system are confirmed by observing the Poincaré cross section and the 0–1 test results of the system. Afterward, the simulation circuit of the fifth‐order system is built in the simulation software Multisim. And the obtained phase diagram is consistent with the results obtained in MATLAB which confirmed the realizability of the system. An FPGA hardware implementation of this fifth‐order eight‐wing system has also been completed at the end of the paper to demonstrate the implementability of the system. And the results are highly consistent with those obtained in the MATLAB software.The fifth‐order chaotic system is combined with the dislocation algorithm and diffusion algorithm to apply to the image encryption system. The encryption system contains a variety of encryption algorithms for secondary encryption of images including Arnold dislocation and repeat‐free scrambling algorithm. The diffusion algorithm includes the addition of the modulus length diffusion and multiplicative operation diffusion based on the GF(257) domain. After a series of performance analysis, the encryption system is proved to have good secrecy effect and great application value in the image encryption neighborhood.

The field of smart home technology has grown by leaps and bounds over the last few decades. This development has made several new products available that can add convenience and security to facility. People became aware of smart home devices with the introduction of the smart thermostat, but these thermostats were only the beginning to smart homes. Smart devices helps people manage their sched-ules, grocery lists, home lighting and even the home security. These devices work together to make life a bit easier as well as safer. One group of such smart home security devices that are gaining popularity are smart door locks. Smart door locks are also seeing new innovative features being added recently where some companies are adding the capability of facial recognition to the locks. With nothing but the face, it’s now possible for a door lock can be operated. As far back as 2016, manufacturers started talk-ing about smart locks with face recognition. The innovation has continued to grow as several main-stream tech devices with facial recognition like the phones and handheld devices with facial recogni-tion unlock features. In smart door locks, the device uses facial recognition, leveraging advanced processing and analytics to identify a person’s facial attributes at the door. KEY WORDS Access control, facial recognition, door locks, security.

This article is about a criterion based on credibility complex fuzzy set (CCFS) to study the prevailing substitution boxes (S-box) and learn their properties to find out their suitability in image encryption applications. Also these criterion has its own properties which is discussed in detailed and on the basis of these properties we have to find the best optimal results and decide the suitability of an S-box to image encryption applications. S-box is the only components which produces the confusion in the every block cipher in the formation of image encryption. So, for this first we have to convert the matrix having color image using the nonlinear components and also using the proposed algebraic structure of credibility complex fuzzy set to find the best S-box for image encryption based on its criterion. The analyses show that the readings of GRAY S-box is very good for image data.

Based on elementary quadratic chaotic flows with no equilibria, this paper proposes a novel four-dimensional hyperchaotic hidden attractor system. Compared with traditional chaotic systems, this system has more complex dynamic behaviors and higher degrees of freedom. Developing this novel system can further expand the research scope of chaos theory and reveal the intrinsic laws and characteristics of chaotic behaviors in higher dimensions.Through nonlinear dynamical analysis, the system exhibits diverse dynamic behaviors with varying parameters and possesses multiple hidden attractors, demonstrating the property of multistability. Subsequently, an analog circuit is constructed using Multisim 14.0 to further validate the system. Finally, image encryption is performed by employing different DNA encoding rules and corresponding operation rules based on chaotic sequences for diffusion. The cipher images are analyzed in terms of histograms, information entropy, and correlation. Experimental results indicate that the proposed algorithm achieves effective encryption and can withstand common attacks.

Design and realization of discrete memristive hyperchaotic map with application in image encryption

Image encryption based on 3D Cubic-Sine Logistic Coupled Map and Improved Chirikov Map

This chapter presents the concept of Smart Home, describes the Smart Home networking technologies and discusses the main issues for ensuring security in a Smart Home environment. Nowadays, the integration of current communication and information technologies within the dwelling has led to the emergence of Smart Homes. These technologies facilitate the building of Smart Home environments in which devices and systems can communicate with each other and can be controlled automatically in order to interact with the household members and improve the quality of their life. However, the nature of Smart Home environment, the fact that it is always connected to the outside world via Internet and the open security back doors derived from the household members raise many security concerns. Finally, by reviewing the existing literature regarding Smart Homes and security issues that exist in Smart Home environments, the authors envisage to provide a base to broaden the research in Smart Home security.

Chaos and compressive sensing based novel image encryption scheme

This paper presents a novel discrete memristor model that incorporates exponential and absolute value functions. A discrete coupled memristor neural network model is constructed based on this memristor design. The periodic and chaotic regions of the discrete neural network model are determined using bifurcation and Lyapunov exponent spectrum. Furthermore, by varying the initial values of the discrete memristive neural network, we observe the coexistence of chaos and periodic attractors, as well as periodic attractors. Additionally, an application to color image encryption based on the discrete system model is given. Security analysis is conducted in the aspects of key space, histogram analysis, correlation analysis, sensitivity analysis, Peak Signal-to-Noise Ratio (PSNR), and information entropy analysis. The analysis results show that the algorithm has a key space size of [Formula: see text], and the information entropy of baboon graph is 7.9993, which is very close to the ideal value of 8. It shows that the image encryption algorithm is feasible and effective. Finally, the implementation of the discrete memristive neural network model is realized using Field Programmable Gate Array (FPGA). The experimental implementation is conducted using the Verilog language on the Vivado 2018.3 platform, and the obtained results align with the numerical simulation results obtained through MATLAB.

A variable-order fractional memristor neural network: Secure image encryption and synchronization via a smooth and robust control approach

To enhance image privacy protection and reduce storage space, this paper proposes an image compression and encryption algorithm that combines compressed sensing, Hachimoji DNA coding, and a 7D hyperchaotic system. The algorithm processes the high-frequency and low-frequency parts after the wavelet transform separately. Sparse high-frequency information is compressed by low-compression measurement. The low-frequency part uses block-compressed sensing, and only a small amount of data is needed to reconstruct a high-quality image. In addition, the measurement matrix is constructed by chaotic sequence, which reduces the demand for memory and transmission bandwidth. To protect compressed data, this paper also presents an image encryption algorithm based on Hachimoji DNA dynamic coding and Joseph dynamic traversal. The algorithm first uses Joseph traversal to disturb the bit position of each pixel dynamically and then implements Hachimoji DNA dynamic coding. At the same time, a new Joseph bidirectional dynamic scrambling-diffusion algorithm is proposed, which gives the encrypted image a uniform grayscale histogram, high information entropy, and sensitivity to resist various attacks effectively.

In this article coset diagrams of the action of PSL(2, Z) on a PL(F_p) are obtained, through parametrization, which yields one of the eight finite generalized triangle groups which are homomorphic images or quotients of PSL(2, Z). Other than this we analyzed the coset diagrams for the parameter for three finite generalized triangle groups. One of the most dependable methods for achieving data security has been the block cipher. S-Boxes constructed using algebraic structure have gained popularity recently because of their advantageous cryptographic properties and high non-linearity have been found in these structures, which attract researchers. With the help of these parametrized actions, a novel algebraic method to create 2^8 S-Boxe was established. The S-Box provides strong cryptographic qualities of nonlinearity 112, differential uniformity 6, linear approximation probability of 0.0576 and differential attack probability of 0.0039. To assess the practical applicability of our S-box, we integrate it into an image encryption scheme and present experimental results to showcase its efficacy in real-world scenarios. When used with an image encryption framework, the following results were obtained: NPCR = 0.9959, UACI = 0.3348, and approx. Entropy 7.98. Therefore, GTG based parametrisation has been shown to be an effective and secure alternative to traditional algebraic construction method for S-Boxes.