ABSTRACT With the explosive growth in the volume of image usage, selective image encryption (SIE) has emerged as an efficient method to enhance encryption efficiency. The challenge of how to identify images containing sensitive content has long been a difficult issue. Deep learning technology, with its powerful semantic extraction capabilities, has naturally become an auxiliary tool for recognizing images containing specific content. This review primarily focuses on recent advancements in SIE integrated with semantic understanding. First, it reviews the current state of development in image ROI encryption. Subsequently, it proposes two semantic‐aware SIE schemes based on image‐to‐image and text‐to‐image search paradigms. The review also introduces evaluation metrics for assessing both the encryption algorithms and deep learning models involved in such SIE systems. Finally, it analyzes potential security issues in SIE, such as privacy protection of deep learning models and leakage of ROI edge regions, as well as possible optimization directions, including model lightweighting and encryption parallelization to enhance efficiency. In conclusion, this review indicates that selective encryption is not limited to ROI‐based approaches but also includes semantic retrieval followed by targeted encryption. Moreover, with the integration of deep learning models, considerations regarding security and efficiency have become more complex, representing key areas for further exploration in future research.

ABSTRACT Freshness is a core quality indicator that determines the utilisation and commercial value of fish products. Traditional fish freshness detection methods are highly subjective and destructive, while existing neural network models suffer from low detection accuracy, unsatisfactory recall and confidence scores and limited generalisation ability. To address these limitations, this paper proposes a novel Dual‐Sensitive Convolutional Neural Network (S‐CNN), where the letter ‘S’ stands for Sensitive. The model simultaneously extracts and fuses discriminative features from fish eye and gill images, capturing subtle freshness differences through a dual‐sensitive feature extraction mechanism. In data preprocessing, all image pixels are normalised to the range [0, 1] to unify numerical scales, stabilise gradient descent and mitigate overfitting. The proposed S‐CNN is composed of seven convolutional blocks, each equipped with batch normalisation, L2 regularisation and a pooling layer; the pooling operation is omitted in the last block to avoid excessive dimensionality reduction. After the flatten layer, a Dropout regularisation module is adopted, and L2 regularisation is applied to all convolutional and fully connected layers. The network uses categorical crossentropy as the loss function. Experimental results demonstrate that the S‐CNN achieves a detection accuracy of 98.70% and an average confidence score of 99.18% on the fish freshness dataset, outperforming other comparative models. The results confirm that the fusion of fish eye and gill features can effectively evaluate fish freshness, providing a reliable method for nondestructive detection and quality assessment of fish products.

ABSTRACT Representation learning has become a cornerstone of artificial intelligence, designed to automatically extract low‐dimensional, meaningful features from high‐dimensional, sparse raw data. By drastically reducing the reliance on manual feature engineering, representation learning enhances model performance across a wide range of tasks. The field has evolved significantly over the past decades, transitioning from early linear methods, such as Principal Component Analysis (PCA), to modern deep learning paradigms powered by neural networks, generative adversarial networks (GANs), and pre‐trained models. Although the rapid development of representation learning has significantly promoted the progress of natural language processing (NLP), computer vision, and recommender systems, the general practitioners still have a poor understanding of its historical background, core principles, and wide range of applications. To some extent, this limits the full development of its potential. To this end, this survey aims to provide a comprehensive and easily understandable overview for a wider audience. This survey conducts a systematic literature review to tease out the evolution of representation learning and analyse its core drivers. At the same time, this survey deeply explains the basic principles of representation learning, and introduces its practical application cases in various fields. This survey also points out the main limitations of current models and prospects the future research directions.

With the popularity of cloud storage, video data, especially videos containing personal information, faces serious security challenges. However, existing video encryption schemes typically adopt full-frame encryption, resulting in high computational overhead, low efficiency, and difficulty balancing privacy protection and lightweight encryption requirements. To this end, a lightweight video encryption scheme based on object detection and adaptive frame multiplexing is designed in this paper. First, the YOLOv8 algorithm is employed to accurately identify multiple human target regions in videos, thereby avoiding the computational resource waste associated with full-frame encryption. Second, combined with SCI-HMC hyperchaotic map, an adaptive frame multiplexing strategy is adopted to realize the dynamic adjustment of chaotic sequences by correlating the encryption process before and after. On this basis, a cube lightweight encryption algorithm based on video frame combination is designed, which is spliced layer by layer in terms of color channels, traversed in terms of 3 layers of data (one video frame), and performs the 3D confusion and 3D mod diffusion sequentially according to the target coordinates, and finally realizes the accurate encryption of the region of interest. The experimental results show that the scheme performs well in terms of practicality and resistance to attacks. The information entropy of the encrypted area is as high as 7.9982, and the encryption speed can be increased to 0.2343 seconds per frame. This deep collaboration framework tightly integrates modern object detection with dynamic encryption processes, providing a balanced security and lightweight solution for cloud video data protection.

To Date, the application of digital twin (DT) in the industrial internet of things (IIoT) has been continuously promoted and deepened, and has become the focus of the industry. IIoT serves as the foundational infrastructure that enables pervasive connectivity, real-time data acquisition, and intelligent control within industrial environments. DTs provide enterprises with an empathetic, virtual environment that enables them to manage and operate their production facilities in a more efficient and intelligent manner. However, there is not a special summary and analysis of the combinability and combination mode of the two. Therefore, this paper firstly sorted out the professional definitions, characteristics and frameworks of IIoT and DT, and deeply analyzed the semantic context of data flow. Secondly, this paper discusses the combinability and combination mode of IIoT and DT, and summarizes the enabling technologies and tools at each layers. Finally, the applications status of DT empowered IIoT in different fields was summarized, and the challenges of the combined application of the two were analyzed.

With the growing demand for cloud services, traditional image privacy encryption methods applied in cloud scenarios reveal two major issues. First, security is often achieved at the expense of visibility, which is incompatible with cloud service scenarios such as information preview and search. Second, there is a lack of design for hierarchical visual privacy for users with different security levels. For the above problems, a multi-level key mechanism is designed and integrated with the YoloV5 network, providing not only multi-level privacy protection for sensitive regions but also achieving a balance between visibility and security in these regions. Simulation results demonstrate that the proposed framework can decrypt images with multi-level visual effects. Performance analysis shows that the framework achieves an adjustable balance between visibility and security, which users can modify by adjusting parameters. Compared to other visibility-security trade-off schemes, this approach offers advantages including strong reversibility, high image size compatibility, adjustable visual effects, and computational efficiency.

Translating the complex dynamics of biological nervous systems into engineered models is crucial for understanding the essence of intelligence and creating human-like artificial intelligence. This article presents a four-dimensional memristive Morris-Lecar model, constructed by coupling a locally active memristor (LAM) with the Morris-Lecar model, which features fast-slow dynamics. Through stability analysis of equilibrium points, the potential mechanism is qualitatively investigated by which three types of equilibrium points trigger neuronal oscillatory activity. Different dimensions of bifurcation diagrams and other numerical techniques reveal that neuronal firing activity and its dynamic characteristics are regulated by three factors: 1) LAM; 2) the slow variable; and 3) ion channels. Based on bursting activity, the fast-slow variable analysis method is employed to study fold and Hopf bifurcations, and the fast-slow dynamics under synergistic control are elucidated. Notably, coexisting attractors are discovered and found to be closely related to LAM. Finally, the neuronal model is implemented on an FPGA to generate firing activities with diverse dynamic characteristics.

To address the high computational costs of full-frame encryption and the risk of exposing sensitive locations in partial encryption, this paper proposes a video selective encryption and steganography scheme based on object detection and image inpainting. First, YOLOv8 is employed to achieve real-time and accurate detection of human targets in video frames. Then, the LIS-HMC hyperchaotic map and a new chaotic-driven interframe chain modulation (CDICM) strategy, combined with a designed row-column interchange and Roller confusion algorithm, are applied to selectively encrypt the target regions. Next, the globally and locally consistent image completion (GLCIC) algorithm is used to restore the background panoramically, eliminating visual discontinuities. Meanwhile, based on the Walsh-Hadamard transform (WHT), a multi-round embedding (MRE) steganography strategy is developed to hide the encrypted information within the restored background. Experimental results show that the encrypted data achieve an information entropy of 7.9925, a steganographic capacity of 0.75 bpp, and a PSNR above 44.91 dB after data embedding, demonstrating that the proposed method provides a new solution for video privacy protection that balances security, real-time performance, and visual naturalness.

With the rapid development of the electronic information industry, massive private data are continuously uploaded to the Internet, posing severe challenges to data security. Thus, a high-capacity private data information protection library based on correlation generator is proposed. For private data uploaded by multiple individuals or organizations, the proposed library enables efficient bulk protection. Through the biometric images held by individuals or organizations, correlation values are generated by the correlation generator, which are combined with such initial values of the four dimensional hyperchaotic system (4DHS) to generate private keys and master keys. The chaotic sequences generated by the iteration of the system are combined with the encryption scheme to provide effective protection of private data information. Afterwards, the scheme is tested for simulation and security, which verify the feasibility and security of the proposed scheme.

In biological decision-making, adaptive behavior arises from the interaction between structured task context, expectation, action selection, and feedback-based learning. While most existing studies reproduce reward-driven responses under clear sensory stimuli, decision formation under weak or absent sensory evidence, such as low or 0% contrast conditions, remains insufficiently explored. To address this issue, this work proposes a biologically plausible memristive decision-making framework based on a block-structured task paradigm. The proposed system adopts a closed-loop architecture composed of four functional modules: stimulus, expectation, action, and reward/punishment. Sensory information is encoded when available, while the expectation pathway provides prior-guided modulation when sensory evidence becomes weak or unreliable. Action selection is generated through competitive integration, and reward–punishment feedback dynamically corrects decision bias and reinforces appropriate responses. Through this hierarchical interaction, stable decision behavior can be achieved even in the absence of explicit sensory inputs. PSPICE simulations are conducted to analyze system dynamics and validate the corrective role of the reward–punishment mechanism under weak and ambiguous conditions. In addition, the proposed framework is demonstrated in an industrial autonomous navigation scenario, illustrating its scalability and applicability for adaptive decision-making under uncertainty.

In the digital age, surveillance videos face severe security threats during transmission. Chaotic systems are often used for encrypted transmission due to their sensitivity to initial conditions and unpredictability. However, existing chaotic encryption schemes are at risk of core information leakage, lack adaptive detection of targets, and are inefficient. To address these issues, this article proposes a lightweight video secondary-encryption scheme integrating YOLOv11 and a Discrete Bi-Neuron Hopfield Neural Network (DBHNN). The YOLOv11 model is used to detect sensitive objects in the video, enabling the scheme to further protect sensitive information. The hyperchaotic sequences generated by DBHNN are used for lightweight secondary-encryption: the point-to-point confusion for target detection objects. Subsequently, enhanced alternating confusion and diffusion are applied to encrypt all frames. The proposed scheme can process batch frames and perform secondary encryption on sensitive objects to enhance security. The simulations and tests show that the proposed lightweight encryption scheme has an encryption speed that is more than 5% better than other schemes, and YOLOv11 is also superior to other models in terms of accuracy and efficiency.

To solve the problem of resource consumption and information security during color image transmission, multiple color images encryption scheme according to a three-dimensional discrete chaotic map with pseudo-random number signal generator and discrete wavelet transform (DWT) compression is proposed. Firstly, dynamics of three-dimensional discrete chaotic map is analyzed and found to provide better randomness for encryption schemes. Next, multiple color images of different sizes are compressed to 1/4 of the original size after DWT processing. The multiple compressed images are merged into a plaintext cube, and the plaintext parameters associated with the cube are generated. The generated plaintext parameters are combined with the chaotic map to form the key. A series of sequences are generated by iteration for image confusion and diffusion to get cipher images. The final simulation results show that recovered plaintext image is still clearly visible even with lossy DWT compression. The security analysis results indicate that this scheme has high level of security protection for color images.

Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This article aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2-D logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multitimescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a 2-D framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%.

Considering the extremely complex physiological environment within neurons, there is feedback from autapse currents as well as the influence of external electromagnetic radiation. In this paper, a second-order memristor is constructed based on the definition of a generic memristor, which two intermediate variables are used to simultaneously model the effects of electromagnetic radiation and autapse on Rulkov neuron, called SOM-Rulkov neuron. The analysis of Lyapunov Exponent spectrum(LEs), bifurcation diagrams, phase diagrams, and iterative diagrams with different parameters that SOM-Rulkov has various types of periodic and chaotic firing patterns and high complexity. In particular, homogeneous extreme multistability is demonstrated with different initial conditions, and the phenomenon is more suitable for image encryption. Furthermore, the SOM-Rulkov map is implemented on the DSP platform. Finally, the SOM-Rulkov map is applied to encrypt the 3D model, which is essentially a sequence generated by homogeneous multistability and the vertex coordinates of the 3D model for different position xor operations. When decrypting, users with different levels of keys can access different visualizations. Experiments show that the scheme has strong security and low time cost.

With the rapid development of the Internet, face recognition technology is widely used, which makes the protection of face database especially important. To protect the recognized faces, a multiface image compression encryption (MFICE) scheme is designed based on the electromagnetic radiation Ktz neuron (ERKN). Since only faces are to be encrypted, they are first extracted. Then the face images are compressed by using semi-tensor product compressed sensing (STP-CS) algorithm, and the compressed images are integrated into a large cube, i.e., a 3-D cube. After that, interface confusion algorithm, 3-D shuffling algorithm, and 3-D diffusion algorithm are sequently performed by using chaotic sequences generated by iteration of ERKN, and finally the ciphertext image cube is obtained. The proposed scheme is evaluated, and it performs well in terms of feasibility and security.

With the popularity of surveillance devices, the security of surveillance video has attracted much attention, and three key issues need to be solved. The videos cannot be synchronized with their encryption effects, while full encryption does not meet the current trend of lightweight algorithms, and customized encryption for multiple specific targets is rarely seen. Inspired by this, a lightweight batch video frame awareness multitarget encryption scheme based on a novel discrete Tabu learning neuron (DTLN) and YoloV5 is designed in this article, the DTLN is in hyperchaotic state within a great range of parameters, which ensures the diversity of key selection and security. At the same time, the coexistence of homogeneous attractors is found, and such attractors are difficult to be successfully recognized by the parameter recognition algorithm, which increases the difficulty for the attacker to obtain the key. The mosaic tracking scheme designed by YoloV5 network can lightweightly encrypt batch frames of multiple types and targets, and users can also customize the encryption targets according to their needs. The simulation results show that the encryption scheme can realize lightweight encryption of multitype and multitarget, and performs well in all the security performance indexes, and has certain advantages compared with other video encryption schemes in terms of performance and functionality.

Diagnosing Parkinson’s disease (PD) remains challenging due to its complex motor symptoms and the reliance on subjective clinical evaluations. To address this issue, this study proposes the multiscale frequency-sharing attention network (MSF-CANet), an end-to-end framework designed to identify PD and healthy control subjects using smartwatch-based inertial sensor data. MSF-CANet integrates a multiscale perception module to capture temporal features of tremors at different frequencies, a frequency-aware module to enhance PD-specific tremor signals within the 3–7 Hz range, and a shared channel attention mechanism to focus on key sensor channels while ensuring computational efficiency. The model was trained and evaluated on the PADS dataset using nested 5-fold cross-validation. The proposed method achieved an accuracy of 92.39% and an AUC of 0.9797, outperforming existing methods. The findings indicate that dual-hand data significantly improves detection performance compared to single-hand data, and dynamic tasks like “Drink from Glass” and “cross and extend both arms” achieved higher accuracy than static activities. These findings underscore the potential of MSF-CANet as a robust, noninvasive tool for real-time PD monitoring through wearable devices.

Discrete chaotic systems based on memristors exhibit excellent dynamical properties and are more straightforward to implement in hardware, making them highly suitable for generating cryptographic keystreams. However, most existing memristor-based chaotic systems rely on a single memristor. This paper introduces a novel discrete chaotic system employing dual memristors, named the 3D memristive cubic map with dual discrete memristors (3D-MCM). The 3D-MCM system demonstrates richer and more intricate dynamical behaviors compared to its single-memristor counterparts, as verified through bifurcation diagrams, Lyapunov exponent spectra, and complexity analyses. Notably, the system exhibits coexisting attractors, substantially enhancing its dynamical complexity. Hardware implementation of the 3D-MCM attractors confirms its feasibility for industrial applications. To illustrate the system’s potential in encryption tasks, this study integrates the quaternary-based permutation and dynamic emanating diffusion (QPDED-IE) scheme with the 3D-MCM for image encryption. Experimental results demonstrate that the QPDED-IE scheme based on the 3D-MCM exhibits strong diffusion and confusion properties, effectively resisting cryptanalytic attacks.

Traditional video encryption methods protect video content by encrypting each frame individually. However, in resource-constrained environments, this approach consumes significant computational resources. To overcome this challenge, this paper proposes a novel method called “Encrypt a story (EAS)”, which aims to enhance encryption efficiency by focusing on encrypting specific segments of the video rather than encrypting each frame. The EAS refers to selecting segments in the time dimension of the video that contain important information or key events for encryption. This method leverages video segmentation techniques to focus encryption efforts on continuous key frames, significantly reducing the consumption of computational resources. To address the need for a large number of key streams during the encryption process, this paper proposes a discrete sinusoidal memristive Rulkov neuron map (DSM-RNM). Through attractor analysis, complexity comparison, Lyapunov exponent, and NIST tests, we validated its ability to generate high-performance pseudorandom sequences, which significantly enhances the security of the encryption algorithm. Notably, the DSM-RNM is shown to exhibit a phenomenon of infinitely coexisting attractors. Furthermore, by constructing a digital circuit to capture the attractors of the DSM-RNM, its potential for industrial applications is demonstrated. Evaluation results show that the EAS saves approximately 90% of the time while ensuring security, exhibiting strong practicality and efficiency

A recently proposed 3D object encryption scheme enables hierarchical decryption, allowing a single encryption to display varied visual effects upon decryption. This has potential applications in complex scenarios where 3D objects require access at different security levels. However, it has two main issues: lossless decryption is not possible due to precision loss from the IEEE 754 standard, and it is customized for a specific AES algorithm, limiting support for others. Motivated by this, a novel paradigm for 3D object encryption with differentiated visual effects upon decryption is proposed. In this paradigm, the precision loss is preserved within the 3D encrypted object, allowing the 3D object after fully decrypting to be identical to the original, and thus it islossless. Meanwhile, the data to be encrypted is reduced to three blocks, which are generalized bitstreams. Bitstream encryption algorithms that do not produce ciphertext expansion can be applied, and it operates independently of the other components in the paradigm, making ituniversal. Finally, a prototype of this paradigm is constructed, using the same encryption algorithm as in the previous scheme for comparative experiments. Two 3D object datasets are used to conduct the experiments and results demonstrate that it achieves lossless decryption and has a time advantage, while also undergoing security analysis and verification against smoothing attacks to confirm the security.

In traditional image encryption, privacy is protected at the expense of all visual content resulting in poor usability. Recently, a novel image encryption concept, thumbnail-preserving encryption (TPE), has been proposed to balance privacy and visual usability after encryption. However, the existing TPE schemes can only encrypt images with specific sizes (related to thumbnail block sizes). The thumbnail block, namely, the image sub-block of equal length and width, and specific means the image size can be divisible by the thumbnail block size. In fact, a little thought reveals that in reality the image size is arbitrary, and it is only by chance that images can be encrypted fully. To this end, we propose a generalized TPE scheme, and it realizes full encryption of images with arbitrary size. Specifically, first of all, a novel block technique called size-compatible-match is proposed. It can be used to accurately match and segment the portion of the image that cannot be encrypted by existing TPE schemes. Secondly, a chaotic system called 2D-GMOS is introduced to greatly reduce the time cost of the encryption and decryption process. Third, the block technique and 2D GM-OS chaotic system are combined with the TPE. The results have demonstrated that images with arbitrary size can be fully encrypted (no leakage of the original image) by the proposed scheme, and the encrypted image has useful visual meaning. Meanwhile, extensive experiments have been done that show the security and robustness of the proposed scheme.

Currently, discrete memristors are a focal point in the study of chaotic maps. Similar to memristors, memcapacitors-another type of memory circuit component-have not received widespread attention in the design of chaotic maps. In this article, we propose a 4-D memcapacitive hyperchaotic logistic map (4D-MHLM) by integrating memcapacitors with the logistic map. The dynamical behavior of the 4D-MHLM is analyzed using Lyapunov exponent analysis, and the impact of different parameters on system performance is discussed. The complexity of generating pseudo-random sequences with the 4D-MHLM is investigated through complexity analysis, including spectral entropy complexity and C0 complexity. Notably, attractor analysis reveals a unique phenomenon of infinite coexisting attractors within the 4D-MHLM. Finally, the chaotic attractor generated by the 4D-MHLM is successfully implemented on a hardware platform. Theoretical analysis and digital circuit implementation results indicate that the 4D-MHLM exhibits rich dynamical behavior and higher complexity, offering significant value for practical applications.

In the previous encryption schemes for sensor information security in WBANs, there are usually the disadvantages of high cost and low time efficiency. At the same time, a large amount of information flooding into the channel also tends to bring huge transmission pressure to the channel. Motivated by the above viewpoints, a scheme that combines semi-tensor product compressed sensing (STP-CS) and session keys (SesKs) for low-cost batch information encryption in WBANs is proposed using image information as an example. The STP-CS can not only compress the information under the guarantee of high reconstruction quality, which reduces the pressure on the channel to transmit the information but also solves the problem of weak compatibility of traditional compressed sensing (CS) on the image size. The SesKs obtained by the communicating parties through the negotiation of the secret keys (SKs) cannot be returned to the SKs for a limited period of time. The cost of achieving one-time pad encryption is also lower compared to the traditional stream cipher encryption, which improves the robustness of wireless broadband network transmission. In terms of the encryption algorithms, the batch image encryption algorithms ensure the timeliness of information transmission security. The results show that the key space of the scheme is as high as 2869, the reconstructed image of$256\times 256$under compression ratio of 0.75 can still obtain PSNR as high as 45.94 dB, and the information entropy of the encrypted image is as high as 7.9978. Compared with the other encryption schemes, the scheme is more suitable to be applied to WBANs because it ensures sufficient security while having the features of low cost, high compatibility, and high efficiency.

With the growing exploration of brain actions, memristive elements with biomimetic properties are urgently needed to estimate the activities of biological synapses. Based on this, a discrete memcapacitor is used as memristive synapses, which are applied in discrete neuron map to construct a memcapacitive-synapse neuron model in this paper. Firstly, the characteristics of the memcapacitor are studied, and its capability to perform memory behavior is demonstrated. Secondly, numerical methods are used to investigate the bionic behaviors and complex dynamical behaviors of the memcapacitive-synapse neuron model, including extreme multistability, and multiple firing patterns, which are all tightly related to the parameters of the memcapacitive-synapse. Finally, the chaotic attractor generated by this neuron model is also implemented based on a DSP hardware platform. It is justified from different perspectives that it is reasonable and feasible to adopt memcapacitor to estimated synapse behaviors.

In this article, a novel locally active memristor (LAM) model is designed and its characteristics are studied in detail. Then, the LAM model is applied to couple FitzHugh-Nagumo (FHN) and Hindmarsh-Rose (HR) neuron. The simple neuron network is built to emulate connection of separate neurons and transmission of information from FHN neuron to HR neuron. The equilibrium point about this FHN-HR model is analyzed. Under the influence of varied parameters, dynamical characteristics for the model are explored with various analysis methods, including phase diagram, time series, bifurcation diagram, and Lyapunov exponent spectrum (LEs). The spectral entropy (SE) complexity and sequence randomness of the model are studied. In addition to observing chaotic and periodic attractors, multiple types of attractor coexistence and particular state transition phenomena are also found in the coupled FHN-HR model. Furthermore, geometric control is used for modulating the amplitude and offset of attractor and neuron firing signals, involving amplitude control and offset control. Finally, DSP implementation is finished, proving digital circuit feasibility of the FHN-HR model. The research imitates the coupling and information transmission between different neurons and has potential applications to secrecy or encryption.

The emergence of the Industrial Internet of Things (IIoT) has greatly improved the efficiency of manufacturing, but it also faces significant security challenges during the operational phase. In this work, a chaos-based image cryptosystem is investigated for IIoT based on graph data structure (GDS) and three-input majority gate (TIMG) to meet the increasing demand for flexible and secure image processing. Some internal relationships between graph and image are first constructed through a flexible adjacency matrix and then the GDS is generalized for image operation to address security defects in classical diffusion structure in image cryptosystem, such as sequential diffusion path and fixed pattern diffusion operators. Technically, a nonsequential diffusion path is established by breadth-first-search traversing the vertices between interblock and intrablock. Then, a multifunctional calculator is designed for a typical diffusive nonlinear primitive based on the logical operator relation of TIMG. In addition, a class discrete maps with fractional-order is provided to enhance the flexibility of multitype key stream selection. Experimental results and analysis for the gray, color, and 3-D images fully demonstrate that a flexible and superior security cryptosystem can be implemented from GDS and TLMG techniques.

In the research of multi-image encryption (MIE), the image type and size are important factors that limit the algorithm design. For this reason, the multi-image (MI) hybrid encryption algorithm that can flexibly encrypt color images and grayscale images of various sizes is proposed. Based on this, combining the back propagation (BP) neural network compression technology and the MI hybrid encryption algorithm, an MI hybrid compression-encryption (MIHCE) scheme can be obtained to reduce the pressure of simultaneous transmission and storage of multiple cipher images. Besides, two chaotic maps are used in the scheme design process. By plotting the phase diagrams under different parameter conditions, the rich variation of the behavior of the chaotic maps in the phase space is exhibited. The MIHCE scheme based on the chaotic maps consists of three parts: 1) compressing the MI cube by using the BP neural network; 2) scrambling the compressed MI cube based on the knight tour problem and chaotic sequences; and 3) diffusing the scrambled MI cube. After the MIHCE is completed, the obtained cipher images are stored and transmitted. Subsequently, the security analysis and compression performance analysis prove the feasibility and safety of the designed compression-encryption scheme.