In the continuous progress of mobile internet technology, medical image processing technology is also always being upgraded and improved. In this field, digital watermarking technology is significant and provides a strong guarantee for medical image information security. This paper offers a robustness zero watermarking strategy for medical pictures based on an Improved NasNet-Mobile convolutional neural network and the discrete cosine transform (DCT) to address the lack of robustness of existing medical image watermarking algorithms. First, the structure of the pre-training network NasNet-Mobile is adjusted by using a fully connected layer with 128 output and a regression layer instead of the original Softmax layer and classification layer, thus generating a regression network with 128 output, whereby the 128 features are extracted from the medical images using the NasNet-Mobile network with migration learning. Migration learning is then performed on the modified NasNet-Mobile network to obtain the trained network, which is then used to extract medical image features, and finally the extracted image features are subjected to DCT transform to extract low frequency data, and the perceptual hashing algorithm processes the extracted data to obtain a 32-bit binary feature vector. Before performing the watermark embedding, the watermark data is encrypted using the chaos mapping algorithm to increase data security. Next, the zero watermarking technique is used to allow the algorithm to embed and extract the watermark without changing the information contained in the medical image. The experimental findings demonstrate the algorithm’s strong resistance to both conventional and geometric assaults. The algorithm offers some practical application value in the realm of medicine when compared to other approaches.

In hyperspectral image (HSI) classification, the high-dimensionality and the complex coupling of spatial-spectral features present severe challenges to existing deep learning methods in terms of accuracy, generalization, and computational efficiency. Researchers have recently explored CNN and Transformer-based methods to overcome these limitations, but CNN's limited receptive field prevents effective modeling of long-range dependencies, while Transformers suffer from high computational cost and inefficiency in high-dimensional data. Motivated by these limitations, the state-space model (SSM) Mamba shows great potential as an efficient alternative for sequence and dependency modeling. Building on this foundation, we propose HyPyraMamba, a novel architecture designed to effectively overcome the above challenges. It integrates the Pyramid Spectral Attention (PSA) module to capture multi-scale key spectral features, thereby reducing interference caused by spectral redundancy. We developed an Adaptive Expert Depthwise Convolution (AEDC) module that enhances the model's ability to express multi-scale spatial-spectral features, and a sequence modeling module, Mamba. In the Mamba module, we utilize the spatial Mamba and spectral Mamba branches to enhance spatial structure and spectral correlation modeling. Extensive experiments on four benchmark HSI datasets demonstrate that HyPyraMamba significantly outperforms several recent state-of-the-art methods and provides a favorable accuracy–efficiency trade-off. In particular, class-wise analyses on spectrally similar land-cover categories (e.g., different soybean and bareland types) show that HyPyraMamba markedly reduces mutual confusion compared with CNN-, Transformer-, and Mamba-based baselines. The code will be available at https://github.com/dekai-li/HyPyraMamba.

Hyperspectral imaging (HSI) can capture a large amount of spectral information at various wavelengths, enabling detailed material classification and identification, making it a key tool in remote sensing, particularly for coastal area monitoring. In recent years, the CNN framework and transformer models have demonstrated strong performance in HSI classification, especially in applications requiring precise change detection and analysis. However, due to the high dimensionality of HSI data and the complexity of spectral-spatial feature extraction, achieving accurate results in coastal areas remains challenging. This paper introduces a new hybrid model, CSTFNet, which combines an improved CNN module and dual-layer Swin Transformer (DLST) to tackle these challenges. CSTFNet integrates spectral and spatial processing capabilities, significantly reducing computational complexity while maintaining high classification accuracy. The improved CNN module employs one-dimensional convolutions to handle high-dimensional data, while the DLST module uses window-based multi-head attention to capture both local and global dependencies. Experiments conducted on four standard HSI datasets (Houston-2013, Samson, KSC, and Botswana) demonstrate that CSTFNet outperforms traditional and state-of-the-art algorithms, achieving overall classification accuracy exceeding 99%. In particular, on the Houston-2013 dataset, the results for OA and AA are 1.00 and the kappa coefficient is 0. 976.The results highlight the robustness and efficiency of the proposed model in coastal area applications, where accurate and reliable spectral-spatial classification is crucial for monitoring and environmental management.

To solve the problem of robustness of encrypted medical image watermarking algorithms, a zero watermarking algorithm based on the discrete cosine transform (DCT) and an improved DarkNet53 convolutional neural network is proposed. The algorithm targets medical images in the encrypted domain. In this algorithm, DCT is performed on the encrypted medical image to extract 32-bit features as feature 1. DarkNet53, a pre-trained network, was chosen for migration learning for the network model. The network uses a fully connected layer and a regression layer instead of the original Softmax layer and classification layer, changing the original classification network into a regression network with an output of 128. With these transformations, 128-bit features can be extracted from encrypted medical images by this network, and then DCT is performed to extract 32-bit features as feature 2. The fusion of features 1 and 2 can effectively improve the robustness of the algorithm. The experimental results show that the algorithm can accurately distinguish different encrypted medical images and can effectively restore the original information from the encrypted watermarked information under traditional and geometric attacks. Compared with other algorithms, the proposed method demonstrates better robustness and invisibility.

With the continuous progress and development in the field of Internet technology, the area of medical image processing has also developed along with it. Specially, digital watermarking technology plays an essential role in the field of medical image processing and greatly improves the security of medical image information. A medical image watermarking algorithm based on an accelerated‐KAZE discrete cosine transform (AKAZE‐DCT) is proposed to address the poor robustness of medical image watermarking algorithms to geometric attacks, which leads to low security of the information contained in medical images. First, the AKAZE‐DCT algorithm is used to extract the feature vector of the medical image and then combined with the perceptual hashing technique to obtain the feature sequence of the medical image; then, the watermarking image is encrypted with logistic chaos dislocation to get the encrypted watermarking image, which ensures the security of the watermarking information; finally, the watermarking is embedded and extracted with the zero‐watermarking technique. The experimental results show that the algorithm can effectively extract the watermark under conventional and geometric attacks, reflecting better robustness and invisibility, and has certain practicality in the medical field compared with other algorithms.

Hyperspectral images (HSIs) inherently possess abundant spectral and spatial information, making them invaluable across diverse remote sensing applications. Recently, deep learning architecture has emerged as the dominant paradigm for HSI classification. However, existing methods face an inherent dilemma: Convolutional Neural Networks (CNNs) excel at extracting local spatial representations but fundamentally struggle to capture long-range dependencies. Conversely, Vision Transformers (ViTs) effectively capture global contexts but incur a prohibitive quadratic computational burden when processing long, continuous spectral sequences. To resolve this dual challenge, we propose GS-MambaNet, a novel decoupled architecture that synergizes Global-Spatial Attention with Grouped-Spectral State Space Modeling. Specifically, a Global-Spatial Attention Module (GSAM) is designed to capture macro-level spatial topologies, while a Grouped-Spectral State-Space Module (GSSM) adaptively partitions spectral channels into sequence groups, leveraging the linear complexity of the Mamba mechanism to efficiently model long-term spectral evolution dynamics. Extensive experiments on four benchmark datasets (Botswana, HongHu, HanChuan, and Pavia University) demonstrate that GS-MambaNet significantly outperforms state-of-the-art methods, achieving superior classification accuracy while maintaining computational efficiency.