To realize a chaotic system in digital world always leads a dynamical degradation of chaotic characteristics. Digital chaotic systems are increasingly utilized in modern cloud computing security architectures and big data analytics frameworks where precision degradation in large-scale, distributed, and heterogeneous environments can severely affect reliability. This paper focuses on the effects of fluctuations on digital chaotic systems and dynamical degradation related issues of those systems. A scheme based on hybrid structure is proposed for the solution to chaos degradation, in which the continuous chaotic system provides intrinsic random fluctuation. Further, the relationship between scaling of intrinsic randomness and chaos with different structure is studied. Symbolic dynamics is applied to rigorously prove that a class of digital systems is chaotic in the presence of fluctuations and to show that scaling behavior of fluctuations determines topological structure of chaos. Specifically, there are two critical fluctuation amplitudes at which topological structure of chaos essentially changes. Moreover, simulation studies are conducted to verify the theoretical results and further compare the effects of different random fluctuations on chaos degradation. These findings contribute to chaos-preserving mechanism designs applicable to precision-sensitive large-scale computing scenarios.

For chaotic cryptography to advance toward practical deployment, it is necessary to pay attention not only to the security issues of chaotic systems but also to problems such as the actual degradation of digital performance and system synchronization. Regarding the security of the chaotic system itself, its characteristic information (including parameters, the structure of coupled chaotic systems, etc.) provides critical entry points for attackers. If these characteristics remain static, chaotic cryptography becomes increasingly vulnerable to cryptanalysis. In this paper, a time-variant stream cipher based on a nondegenerate and coupled chaotic system is proposed. The analog-digital hybrid technique is employed to solve the dynamical degradation in the digital field, and digital adaptive pulse control for synchronization. The coupling structure, delay, and parameter of the coupled chaos are dynamically varied following a time-variant mechanism to enhance the security. The practical effectiveness is demonstrated by FPGA-FPAA collaborative hardware design, wherein an event-triggered synchronization scheme is also presented for hardware implementation. Experimental results and theoretical analyses show that the proposed cipher can provide high-quality and robust keystreams for wide cryptographic applications. The construction strategy and components of the proposed cryptosystem are beneficial to motivate chaotic cipher designs and applications.

Chaotic systems commonly demonstrate significant potential for cryptographic applications due to their desirable properties of sensitivity to initial conditions, ergodicity, and pseudo-randomness. However, practical deployment of chaos-based cryptography in lightweight application scenarios is critically hindered by the dynamic degradation of chaotic systems under finite-precision digital implementations and the often excessive hardware overhead of existing enhancement schemes. To address this challenge, this article proposes a novel fractal-driven chaotification model (FDCM). By leveraging fractal sequences to perturb the existing chaotic maps that serve as seed maps, the FDCM can generate numerous new chaotic maps. Fractal sequences with simple logics can induce digital chaotic maps to generate complex behaviors. The FDCM is a general framework for constructing lightweight and highly degenerate-resistant chaotic maps. Theoretical analyses and numerical experiments indicate that the FDCM exhibits favorable chaotic characteristics and nondegenerate properties. To further demonstrate low hardware consumption of the FDCM, a novel lightweight pseudorandom number generator (PRNG) is designed based on the chaotic map generated by the FDCM and implemented on the field programmable gate array. Performance analyses show that our PRNG exhibits excellent randomness even with 8-bit precision that requires significantly fewer hardware resources. Compared with recently proposed PRNGs, the proposed PRNG requires low hardware consumption and offers good randomness. The results demonstrate that our developed FDCM is more suitable for lightweight application scenarios such as Internet of Things and real-time secure communications than existing enhanced chaotic maps.

Nowadays, the application of neural networks, genetic operations, and chaos to image encryption algorithms has become a focused area of frontier research. The neuronal mechanism and deep neural network have good nonlinear characteristics and meet the anti-aggression demands for cryptosystem security. What cannot be ignored is that due to multiplications between matrices in the computation of neuronal mechanisms and deep neural networks, the activation function cannot precisely recover the image, and the direct application of image encryption by neurons and deep neural networks cannot obtain secure results. Moreover, the performance of the adopted chaotic systems also determines the safety level of the cryptosystem. The fixed coupling structures in current research make the dynamic behavior easy to predict, bringing vulnerability to cryptosystems. Considering the aforesaid flaws, an image encryption algorithm using the neural-network pattern, genetic algorithm, and time-varying coupling structure of the chaotic system is proposed, helping improve the security and efficiency of our cryptosystem. Firstly, a time-varying coupled chaotic system is proposed, and highly stochastic sequences are used as keystreams. Then, the keystreams are processed using the neural-network mode and genetic operation to complete scrambling and diffusing. Ultimately, the simulations and security analyses indicate that our method reaches a highly safe level, which shows high efficiency and strong security, and meets the demands of various security applications.

Recently, the color image encryption algorithm based on chaos theory has become the focus of current research. When encrypting color images, the common practice is to treat color images as different gray components and process them severally, which results in more redundancy and low efficiency. The security of chaotic cryptosystems also depends on the performance of the chaotic systems adopted. The structures of many current chaotic systems are fixed, making their behaviors highly predictable. Additionally, the range of chaotic region parameters is limited and discontinuous. To solve the above-mentioned problems, a new color image encryption algorithm (CIEA) using fractal and chaos theory is presented, which fully considers the inherent connection among the RGB components of color images. First, we propose a variable-structure discrete hyperchaotic system (VSDHS) to solve the dilemma encountered by existing chaotic systems. The excellent dynamic properties of VSDHS are verified by rigorous mathematical proof and simulation performance analyses. Then, VSDHS-CIEA is designed using fractal theory and VSDHS. The algorithm makes full use of the inherent connection among the different components of color images and performs cross-plane confusion. Simulations and performance analyses prove that VSDHS-CIEA has higher security and better performance than some representative image encryption algorithms.

Chaotic systems have random-like properties and are extremely sensitive to initial conditions. This property makes chaos have a wide range of potential applications in fields such as cryptography and security technologies, especially in digital watermarking techniques for enhancing multimedia copyright protection. However, the degradation of digital chaotic systems may hinder their effective application in cryptography. To overcome this problem, an innovative control strategy is proposed in this work. By combining two delayed states with dynamic binary dissimilarity operations, this strategy aims to significantly enhance the complexity of chaotic systems. On this basis, a pseudo-random number generator (PRNG) is designed. The sequence generated by this PRNG successfully passed the standard tests such as NIST SP800-22 and TestU01. In addition, a new digital watermarking scheme is proposed by combining it with discrete wavelet transform and two-dimensional variational mode decomposition (2D-VMD). The satisfying performance of the proposed scheme is demonstrated by analyzing the invisibility of the watermarking scheme and its resistance to various attacks.

For the optimisation of video splicing quality in video splicing, an improved optimal stitching video splicing method is proposed. The method first extracts corner points and feature points using the improved FAST algorithm, and accurately matches these feature points by combining violent matching with GMS feature point matching; adopts a graph cutbased algorithm to transform the stitch line search problem into an energy minimisation problem to ensure the reasonableness and optimality of the stitch line position; and constructs the solution step by step in overlapping regions by means of a dynamic planning algorithm, retaining the optimal solution at each step, and finally find the globally optimal suture position. To further enhance the suture quality, the method smoothes the sutures by morphological operations (e.g., swelling and erosion) and implements fine pixel-level adjustments to eliminate artefacts and chromatic aberrations. After determining the optimal overlap region and suture line location, an advanced image fusion algorithm is used to ensure that the transition of video frames in the overlap region is natural and seamless, avoiding splice marks. The experimental results show that the method can effectively improve the quality of video stitching.

High-dimensional chaotic maps offer a larger parameter space, increased complexity, and enhanced resilience against dynamical degradation compared to their one-dimensional counterparts. Therefore, they are gradually replacing one-dimensional chaotic maps in various applications. However, many methods for generating high-dimensional chaotic maps lack mathematical proofs, which cannot theoretically ensure their chaotic nature. Even high-dimensional chaotic maps with theoretical support often lack global transitivity and exhibit local chaos. Applying such chaotic maps in chaos-based stream ciphers or random number generators results in poor randomness of generated chaotic sequences, reduced internal state space, and numerous weak keys, which is not ideal. This paper proposes a systematic method for constructing high-dimensional chaotic maps (called dispersal maps). The paper proves that the maps constructed are topologically mixing across the entire space and are hyper-chaotic on an invariant subset of full measure. These properties make them satisfy almost all definitions of chaos, and their chaotic dynamical behavior is global: exhibiting transitivity across the entire phase space rather than a local subregion, a dense scrambled subset rather than a tiny one, and being hyper-chaotic almost everywhere rather than on a local attractor. Therefore, dispersal maps can improve the existing problems of locally chaotic maps in application. The experiments also indicate that dispersal maps exhibit ergodicity on the phase space, with highly uniform trajectory distributions and sensitivity to initial perturbations. The findings provide researchers with ideal chaotic maps and a feasible method for constructing high-dimensional chaotic maps with global chaos.

The design of the lattice coupling mode in current spatiotemporal chaotic systems lacks dynamic characteristics. When it is applied to the cryptosystem, its chaotic performance defects weaken the security of the cryptosystem. In this context, it is urgent to design new coupling rules and secure cryptosystems based on fractal and chaos theory. In this paper, a kind of sorting vector based on the fractal sorting matrix and fractal curve, the Hilbert sorting vector (HSV), is proposed creatively, and its iterative generation process is introduced. HSV is irregular and infinitely iterable. According to the requirements of the actual situation, HSV has a flexible adjustment vector length, which improves the multiplicity and efficiency of changing information positions. Then, HSV is used to reconstitute the the interaction of nodes during iteration in a new spatiotemporal chaotic system named Hilbert-sorting-vector coupled map lattice (HSVCML). Using this new sorting vector, the dynamic characteristics of spatiotemporal chaotic systems can be effectively improved. This is proved by comparing their Lyapunov exponent, Kolmogorov-Sinai entropy, bifurcation diagram, and information entropy with the coupled map lattice (CML). Moreover, the rich spatio-temporal behaviours of HSVCML are studied. Therefore, HSVCML is more appropriate for image encryption than CML. Finally, HSV is combined with a spatiotemporal chaotic system to frame an image encryption method. For the purpose of proving the effectiveness and security of this algorithm against different types of attacks, a large number of tests related to security analysis and time complexity analysis are carried out. Simulation results prove that our encryption algorithm is more secure and efficient than the previous algorithms and can resist various attacks.

In this essay, a model-level fusion technique of multi-modal physiological signals using Multi-Head Attention is studied. A framework that utilizes multi-model physiological signals for the task of emotion classification is proposed. First, the GCRNN model, which combines the Graph Convolutional Network (GCN) and the Long and Short Term Memory (LSTM), captures the unique features of electroencephalogram (EEG) signals. The spatial and temporal information that makes up impulses from the EEG can be captured precisely by such a technique. The CCRNN model, which combines the Convolutional Neural Network (CNN) integrated with the Channel-wise Attention and the LSTM, is used for peripheral physiological signals. The model can extract useful features from peripheral physiological signals and automatically learn to weigh the importance of various channels. Finally, Multi-head Attention is employed to fuse the output of the GCRNN and CCRNN methods. The Multi-head Attention can automatically learn the relevance and importance of different modal signals and weigh them accordingly. Emotion classification is implemented by adding a component of Softmax to map what the model produced to discrete emotion categories. The DEAP dataset was utilized in this study for experimental verification, and the results indicate that the method using multi-modal physiological signal fusion is substantially greater in precision than the technique using simply EEG signals. Additionally, the Multi-head Attention fusion method performs better than previous fusion techniques.

The research of adversarial examples has extended from image to text in the last few years. However, these attacks are typically limited to the English language and simple substitution strategies. To further expose the vulnerability of NLP models, we study the linguistic characteristics of Chinese, the quintessential ideogram with over 1.2 billion native speakers. Accordingly, a novel attack framework named ZHDeceiver is proposed to generate Chinese adversarial examples from the perspective of morphology, phonetics, semantics, and basic transformation. In particular, a CNN-based Siamese Network is integrated to ameliorate the quality of adversarial examples. To elaborate the validity of ZH-Deceiver, extensive experiments are conducted on two datasets. Compared with four benchmarks such as Genetic, PWWS, TextBugger, and SememePSO, our attack achieves impressive performance on effectiveness, efficiency, imperceptibility, and human evaluation by deceiving seven AI models including CNN and BERT. Furthermore, the transferability, as well as the robustness, is further analyzed and the former is successfully applied to attack three commercial APIs: Tencent, ALi, and Baidu. ZH-Deceiver acts as a wake-up call for multilingual processing models, and tangibly extends the application and methodology of adversarial textual attack. (c) 2022 Elsevier Ltd. All rights reserved.

Image encryption based on chaotic systems and DNA encoding is the focus of current research. However, existing image encryption algorithms lack attention with regard to the application efficiency of scrambling and diffusing operations based on DNA encoding and chaos, resulting in more redundancy and low efficiency. In practical applications, when chaotic systems are simulated on digital platforms, their chaotic performance will be weakened because of the presence of dynamic degradation. In addition, many chaotic systems have simple control parameters, making their behavior easily predictable. These flaws weaken the security and efficiency of cryptographic systems. For the purpose of solving the above problems, a new simultaneous confusion-diffusion image encryption algorithm (SCD-IEA) is presented that fully considers application efficiency and security. Firstly, a compound-coupled chaotic system (CCCS) is used to solve the problems listed above. Following that, a novel image encryption method is proposed using DNA coding and CCCS based on simple logistic and sine mappings. The algorithm improves application efficiency and performs synchronous scrambling diffusion encryption based on DNA coding. Simulation and security analysis results show that our SCD-IEA contributes to a higher level of security and superior performance than some representative methods.

High-quality random number generators (RNGs) are essential in many fields. To overcome the drawbacks in instability of the true RNGs and periodicity of the pseudo-RNGs, based on an analog–digital hybrid chaotic entropy source, an aperiodic hybrid RNG is proposed. The hybrid source is a nondegenerate chaotic system that consists of a delay-coupled digital chaotic map and the analog anticontrol. The anticontrol strategy that considers practical implementation is rarely studied. In this paper, the construction strategy and anticontrol mechanism are well designed and can be regarded as a general methodology to realize multi-dimensional chaotic systems without performance degradation. The proposed system presents good chaotic behaviors in the digital world and has great advantages when realized on hardware platforms. The detailed software simulation and hardware implementation are both presented to verify the effectiveness of the scheme. Due to the excellent properties of the chaotic source, without complicated post-processing, the proposed hybrid RNG can generate high-quality true random bits steadily at relatively low precision and shows robustness to the parameter fluctuation, therefore it is suitable for cryptography and other potential applications.

In recent decades, an avalanche of chaos-based cryptosystems have been proposed for information security. Most of these systems are not immune to the dynamical degradation of digital chaos and many have been shown to suffer from a lack of security. In this paper, a stream cipher system based on an analog–digital hybrid chaotic system is presented. The hybrid model can construct digital chaotic maps without degeneration and guarantee synchronization of analog chaotic systems for successful decryption. Moreover, focusing on the characteristics of low-dimensional chaotic maps, a modified three-dimensional Logistic map is proposed to improve the weaknesses of uneven distribution, low complexity and limited parameter space. Combining the three-dimensional Logistic map and the hybrid model, the proposed stream cipher has advantages of huge key space, virtually infinite cycle length and tight security. In particular, it is not affected by the dynamical degradation. Performance and security analyses indicate that the proposed stream cipher is highly resistant to various chaos-based attacks and cryptanalytic attacks.

The research of adversarial attacks in the text domain attracts many interests in the last few years, and many methods with a high attack success rate have been proposed. However, these attack methods are inefficient as they require lots of queries for the victim model when crafting text adversarial examples. In this paper, a novel attack model is proposed, its attack success rate surpasses the benchmark attack methods, but more importantly, its attack efficiency is much higher than the benchmark attack methods. The novel method is empirically evaluated by attacking WordCNN, LSTM, BiLSTM, and BERT on four benchmark datasets. For instance, it achieves a 100% attack success rate higher than the state-of-the-art method when attacking BERT and BiLSTM on IMDB, but the number of queries for the victim models only is 1/4 and 1/6.5 of the state-of-the-art method, respectively. Also, further experiments show the novel method has a good transferability on the generated adversarial examples.

The characterization and understanding of online social network behavior is of importance from both the points of view of fundamental research and realistic application. In this manuscript, we propose a stochastic differential equation to describe the online microblogging behavior. Our analysis is based on the microblog data collected from Sina Weibo, which is one of the most popular microblogging platforms in China. Especially, we focus on the collective nature of the microblogging behavior, which embodies itself as the periodic patterns, the stochastic fluctuations around the baseline, and the extraordinary jumps in the analyzed data. Compared with existing works, we use time dependent parameters to facilitate the periodic feature of the microblogging behavior and incorporate a compound Poisson process to describe the extraordinary spikes in the Sina Weibo volume. These distinct merits lead to significant improvement in the prediction performance, thus justifying the validity of our model. This work may offer an alternative route towards the future detection of the anomalous behavior in online social network platforms.

Thermo-acoustic (TA) ultrasound has lots of advantages over traditional electric-acoustic ultrasound. In this work, by using a full-field formulas derived for acoustic field of TA emission from arbitrary source based on a thermally-mechanically coupled model, the basic natures of TA emission from nanofilm are studied via carbon nanotube (CNT) film. It is found that the TA sound pressure level (SPL) is fluctuated in near field and attenuated in far field, the SPL fluctuation in near field results from the interference of TA waves emitted from every element of film, which directly leads to the existence of, in point of technique, the most important nature of TA wave--flat frequency response at certain condition, and in far field, all the sound-emitting films can be regarded as point sources. These researches are significant for understanding TA wave and developing a variety of TA devices.

Chaos is a paradigm shift of all science, which provides a collection of concepts and methods to analyze a novel behavior that can arise in a wide range of disciplines. However, most of researches in simulations and applications of chaos are performed on finite-state automata, which inevitably causes chaos to collapse. Here we present a hybrid model by controlling digital system with continuous chaotic system to construct chaos on finite-state automata. A new concept and method named Generalized Symbolic Dynamics (GSD) is proposed to target the hybrid system. Based on GSD, a rigorous proof is given that the controlled digital system is chaotic in the sense of Devaney. Moreover, analog-digital hybrid circuit is built for the digital chaotic system. Finally, a simple pseudorandom number generator is designed as a proof of concept. Results show that the proposed generator has good performance for cryptography. Such digital chaotic systems, which are not subject to degradation, could pave the way for widespread applications of chaos.

When chaotic systems are implemented on finite precision machines,it will lead to the problem of dynamical degradation.Aiming at this problem,most previous related works have been proposed to improve the dynamical degradation of low-dimensional chaotic maps.This paper presents a novel method to construct high-dimensional digital chaotic systems in the domain of finite computing precision.The model is proposed by coupling a high-dimensional digital system with a continuous chaotic system.A rigorous proof is given that the controlled digital system is chaotic in the sense of Devaney's definition of chaos.Numerical experimental results for different high-dimensional digital systems indicate that the proposed method can overcome the degradation problem and construct high-dimensional digital chaos with complicated dynamical properties.Based on the construction method,a kind of pseudorandom number generator (PRNG) is also proposed as an application.