The increasing deployment of digital healthcare systems has led to the continuous transmission of highly sensitive patient data, raising urgent concerns about data leakage in high-noise, high-loss, and dynamically changing. Existing privacy-preservation techniques often struggle to provide robustness, low overhead, and real-time responsiveness under high jitter and packet loss, limiting their effectiveness in rapid detection and accurate tracing of leaks. To address these challenges, we propose a Reinforcement Learning-Driven Joint Watermarking Framework (RLDJ-W). First, it utilizes a reinforcement learning strategy to adaptively modulate the watermark embedding interval, ensuring both invisibility and enhancing the watermark’s survivability in harsh channels. Then, it leverages Bi-LSTM to capture and model multi-granularity time-series features of network flows, thereby dynamically evaluating the invisibility of the watermark flows. Finally, a high-performance decoding network based on MLP is designed to achieve efficient and accurate watermark information extraction. Experimental results demonstrate that the watermarking capacity of RLDJ-W achieves 2.25 bit/s, requiring only an average of 5.88 packets per bit of watermark. It also maintains over 85% detection accuracy even under 100ms delay jitter and 40% packet loss, consistently outperforming state-of-the-art baselines.

The exponential growth of sensitive patient information and diagnostic records in digital healthcare systems has increased the complexity of data protection, while frequent medical data breaches severely compromise system security and reliability. Existing privacy protection techniques often lack robustness and real-time capabilities in high-noise, high-packet-loss, and dynamic network environments, limiting their effectiveness in detecting healthcare data leaks. To address these challenges, we propose a Swarm Intelligence-Based Network Watermarking (SIBW) method for real-time privacy data leakage detection in digital healthcare systems. SIBW integrates fountain codes with outer error correction codes and employs a Multi-Phase Synergistic Swarm Optimization Algorithm (MPSSOA) to dynamically optimize encoding parameters, significantly enhancing the robustness and interference resistance of watermark detection. Additionally, a reliable synchronization sequence and lightweight embedding mechanism are designed to ensure adaptability to complex, dynamic networks. Experimental results demonstrate that SIBW achieves over 90% detection accuracy under high latency jitter and packet loss conditions, surpassing existing methods in both robustness and efficiency. With a compact design of only 3.7 MB, SIBW is particularly suited for rapid deployment in resource-constrained digital healthcare systems.

Object detection technology plays an essential role in Unmanned Aerial Vehicle (UAV) sea search and rescue missions, which aim to quickly locate crucial targets such as trapped people and ships at sea in the complex marine environment. However, due to the restricted view angle of the UAV and the specificity of the working environment, the remote sensing images captured in the marine environment are characterized by small targets and considerable interference on the sea surface, which brings significant challenges to the UAV sea rescue mission. To address this issue, we propose an object detection model based on the Transformer architecture in this paper. The model takes FCDS-DETR as the baseline and introduces a two-dimensional Gaussian probability density distribution as an additional attention mechanism to speed up the model’s location of suspicious targets in the image to improve the detection accuracy of the model for targets. At the same time, the denoising training method is introduced in the model’s training process to stabilize the bipartite graph matching and promote the convergence of the model. On the SDS ODv2 and AFO object detection datasets, our model achieves an average precision of 51% and 54.5%, respectively, which is an improvement of 5.7% and 4.4% compared to the baseline model’s performance on these datasets.

In the rapidly advancing landscape of intelligent medical technology, high-precision and widely applicable medical image segmentation techniques are pivotal in advancing personalized treatment plans and enhancing patient experiences. To address inherent obstacles, such as complex structures and features that are difficult to capture in segmentation tasks, we first propose a CMH module that merges convolutional neural networks (CNNs) with state space models (e.g., Mamba). This module hierarchically integrates CNN’s local priors into the Mamba layer, adeptly capturing specific details of target objects and broader contextual information from different perspectives. Subsequently, we introduce an information fusion module based on Mamba (MIH), aimed at dynamically fusing feature information from different paths and levels to enhance feature expression and discrimination. Building upon the abovementioned modules, we develop an unexplored segmentation model, CMHNet, capable of flexibly capturing feature maps of varying scales and angles from medical images. In the experiments, we present CMHNet’s distinguished performance in segmentation tasks through massive experiments on four medical datasets.