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.

As the application of image emotion recognition technology grows increasingly widespread, emotional data faces potential privacy risks during transmission and storage. Images depicting negative emotions are particularly prone to revealing an individual is psychological state and sensitive information. Therefore, effective security protection for such images is of practical importance. This paper design a chaotic system and use CNN-based recognition to select the images requiring enhanced protection. First, a novel second-order memristor is designed and coupled with a neuron model to construct a chaotic system (SOM-Rulkov). Analysis of its phase diagram, bifurcation diagram, and Lyapunov exponent spectrum indicates that SOM-Rulkov exhibits rich dynamical characteristics, which can provide a pseudo-random key stream with good performance for encryption algorithms. Then, using a CNN to recognise emotions in images, employing the identified negative emotion images as encryption images to enhance data security during transmission and storage. During the encryption process, This paper proposes an enhanced diffusion structure with a parallel pool mechanism that enables four-directional diffusion to improve encryption efficiency. Experimental results show that the proposed scheme achieves strong security and high speed for emotional image privacy protection.

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.

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.

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.

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%.

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.

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

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.