The rapid advancement of 5G technology has led to a significant increase in the generation and transmission of visual image data, underscoring the growing need for robust privacy protection. However, existing chaotic encryption methods often suffer from simplicity and limited security because they rely on single-layer encryption approaches. To address these challenges, this paper introduces a multi-layer image encryption algorithm that leverages the Logistic-Sine chaotic Map (LSCM) and cellular automata for enhanced security. Initially, the R, G, and B channels of an image are extracted and subjected to independent row and column transformations, creating a new, scrambled matrix. Subsequently, the proposed LSCM, which integrates logistic and sine maps to overcome periodic vulnerabilities, generates a rich chaotic sequence for XOR-based encryption. Finally, cellular automata further enhance the obfuscation, increasing the algorithm’s complexity and attack resistance. Experimental results demonstrate that the image encryption algorithm achieves high security, strong robustness against noise and data loss, and superior performance in statistical and correlation analyses. These findings suggest that the algorithm is well-suited for protecting visual data in networked environments, offering significant application value in image privacy and security.

Current mainstream computer vision algorithms focus on designing suitable network architectures and loss functions to fit training data. However, the accuracy of small object detection remains lower than for other scales, and the design of convolution operators limits the model’s performance. For UAV small object detection, standard convolutions, due to their fixed kernel size, cannot adaptively capture small object spatial information. Many convolutional variants have scattered sampling points, leading to blurred boundaries and reduced accuracy. In response, we propose HawkEye Conv (HEConv), which utilizes stable sampling and dynamic offsets with random selection. By varying the convolution kernel design, HEConv reduces the accuracy gap between small and larger objects while offering multiple versions and plug-and-play capabilities. We also develop HawkEye Spatial Pyramid Pooling and Gradual Dynamic Feature Pyramid Network modules to validate HEConv. Experiments on the RFRB agricultural and VisDrone2019 urban datasets demonstrate that, compared to YOLOv10, our model improves AP₅₀ by 11.9% and 6.2%, AP_S by 11.5% and 5%, and F1-score by 5% and 7%. Importantly, it enhances small object detection without sacrificing large object accuracy, thereby reducing the multi-scale performance gap.

With the rapid proliferation of IoT technology, cybersecurity challenges have become increasingly prominent. Traditional centralized network intrusion detection systems (NIDS) exhibit significant limitations including privacy risks and inadequate modeling of spatiotemporal correlations. This paper proposes FedT-DQN (Transformer-based Federated Double Q-Network), a novel federated reinforcement learning framework for dynamic intrusion detection. The method incorporates: (1) A Transformer encoder as federated aggregator using self-attention mechanisms; (2) A dual-layer Q-network architecture decomposing detection into feature extraction and decision optimization; (3) Soft Actor-Critic (SAC) integration for local training considering system heterogeneity. Experimental results on four benchmark datasets show that FedT-DQN achieves over \(99\%\) detection accuracy with enhanced F1 scores and reduced false positive rates, all while maintaining data privacy. The source code for this study is available at https://github.com/BuLaTaa/FedT-DQN-in-IDS..

Micro-expressions are extremely subtle and short-lived facial muscle movements that often reveal an individual’s genuine emotions. However, micro-expression recognition (MER) remains highly challenging due to its short duration, low motion intensity, and the imbalanced distribution of training samples. To address these issues, this paper proposes a Global–Local Feature Fusion Network (GLFNet) to effectively extract discriminative features for MER. Specifically, GLFNet consists of three core modules: the Global Attention (LA) module, which captures subtle variations across the entire facial region; the Local Block (GB) module, which partitions the feature map into four non-overlapping regions to emphasize salient local movements while suppressing irrelevant information; and the Adaptive Feature Fusion (AFF) module, which employs an attention mechanism to dynamically adjust channel-wise weights for efficient global–local feature integration. In addition, a class-balanced loss function is introduced to replace the conventional cross-entropy loss, mitigating the common issue of class imbalance in micro-expression datasets. Extensive experiments are conducted on three benchmark databases, SMIC, CASME II, and SAMM, under two evaluation protocols. The experimental results demonstrate that under the Composite Database Evaluation protocol, GLFNet consistently outperforms existing state-of-the-art methods in overall performance. Specifically, the unweighted F1-scores on the Combined, SAMM, CASME II, and SMIC datasets are improved by 2.49%, 2.02%, 0.49%, and 4.67%, respectively, compared to the current best methods. These results strongly validate the effectiveness and superiority of the proposed global–local feature fusion strategy in micro-expression recognition tasks.

As cloud computing proliferates, outsourced data faces severe security threats, yet existing searchable encryption (SE) schemes rely on classical hardness assumptions, centralized trust authorities, and static access control, leaving critical gaps in quantum resistance, single-point-of-failure prevention, and dynamic permission management. To address these limitations, we propose BL-ABSE, a blockchain-enhanced, lattice-based attribute-based searchable encryption framework. BL-ABSE employs the Ring Learning With Errors (RLWE) problem as its security foundation and applies the Number Theoretic Transform (NTT) to reduce polynomial multiplication from $O\left(n^2\right)$ to $O(nlogn)$. To eliminate single-point trust risks, the framework further integrates a $(t,n)$ threshold key protocol across an edge-node consortium governed by Practical Byzantine Fault Tolerance (PBFT) consensus. A smart-contract-maintained on-chain revocation list enables permission withdrawal via a single blockchain transaction without re-encryption. Experimental evaluation demonstrates that commitment generation requires approximately 23 ms at $n=1024$, search latency scales linearly at roughly 29 µs per record, and revocation completes in approximately 2 s regardless of system scale. Formal security proofs under the quantum polynomial-time (QPT) adversary model reduce six security properties—index indistinguishability, query privacy, threshold key security, Byzantine fault tolerance, audit immutability, and revocation immediacy—to the hardness of RLWE and the Short Integer Solution (SIS) problems. To the best of our knowledge, BL-ABSE is the first framework to simultaneously achieve post-quantum security, attribute-based access control, decentralized key management, instant revocation, and immutable auditing within a single unified framework. We further conduct threshold parameter verification, end-to-end revocation latency decomposition, blockchain throughput stress testing, search-pattern leakage quantification, and communication/storage overhead analysis, providing a comprehensive evaluation of both performance and security trade-offs. We explicitly characterize the search-pattern leakage inherent in the deterministic commitment design as a correctness–privacy trade-off and discuss mitigation directions.