To achieve watermark synchronization from an image with perspective distortion is a challenge. This paper proposes a screen-cam robust and blind watermarking scheme for tile satellite images, which means we do not need any user interaction or additional information in watermark detection. To achieve this, at the watermark embedding side, we divide tiles into synchronization tiles and message tiles, and propose a discrete Fourier transform (DFT) based embedding method to cope with the quality degradation caused by screen-cam process. At the extraction side, based on the idea of template matching, we first use a synchronization response index to estimate an appropriate scale level and positions of embedded synchronization watermarks from the noise component, which is estimated by Wiener filter. Then, we propose verification, selection, and precise locations methods to ensure the effectiveness and accuracy of synchronization detection results. After that, we can extract the regions of message tiles. Finally, we extract watermark message based on the local max value from the DFT domain of the noise component. Experimental results demonstrate the validity of the proposed scheme against common attacks and screen-cam attack with a tripod as well as handhold shooting.

The existing commutative encryption and watermarking (CEW) methods based on feature invariants can achieve both the robustness of the watermarking algorithm and the security of the encryption algorithm. However, they are only applicable to the raster data such as images, videos, etc. In particular, the organization structure and storage structure of vector map have not been considered in these methods. Therefore, they cannot be used for vector map. This paper derives two feature invariants to solve this problem, which are the sum of inner angles and the storage direction of two adjacent objects according to the inherent characteristics of vector map. Based on these two feature invariants, a new CEW method is proposed in this paper, which includes the feature invariants based watermarking algorithm and the perceptual stream cipher based encryption algorithm on coordinates. Since the coordinate values used in encryption and the feature invariants used in watermarking are independent of each other, the commutativity is achieved for the proposed CEW method. The experiments are given to verify that the proposed CEW method can achieve the commutativity between encryption and watermarking without deteriorating accuracy of data. Besides, it has been verified that the proposed method is more robust to rotate, scaling, translation, and projection transformation compared with the existing CEW methods and has high security. The proposed algorithm has good scalability of encryption, and arbitrary encryption methods based on encrypting the coordinate values can be applied without affecting the extracted feature invariants.

In order to categorize feature classes by capturing subtle differences, hyperspectral images (HSIs) have been extensively used due to the rich spectral-spatial information. The 3D convolution-based neural networks (3DCNNs) have been widely used in HSI classification because of their powerful feature extraction capability. However, the 3DCNN-based HSI classification approach could only extract local features, and the feature maps it produces include a lot of spatial information redundancy, which lowers the classification accuracy. To solve the above problems, we proposed a spatial attention network (SATNet) by combining 3D OctConv and ViT. Firstly, 3D OctConv divided the feature maps into high-frequency maps and low-frequency maps to reduce spatial information redundancy. Secondly, the ViT model was used to obtain global features and effectively combine local-global features for classification. To verify the effectiveness of the method in the paper, a comparison with various mainstream methods on three publicly available datasets was performed, and the results showed the superiority of the proposed method in terms of classification evaluation performance.

With the widespread application of the Internet of Things (IoT), data processing has gradually migrated to edge devices that are closer to the data source. This shift has significantly improved the ability of real-time data analysis while effectively reducing bandwidth requirements and latency. Furthermore, federated learning (FL) has been introduced as a decentralized training method to achieve collaborative training of multiple devices while ensuring local data privacy. However, malicious clients in FL may theft trained models for unauthorized use, which causes model misuse or copyright challenges. To address these issues, this article proposes malicious client detection, leakage tracing, and copyright verification (FedMLC), a server-side white-box watermarking scheme. FedMLC utilizes the embedded watermark at different stages to achieve both traceability and copyright verification, simplifying the watermarking process. Additionally, the watermarking can also detect malicious clients in FL. Specifically, FedMLC uses the regularization term to guide the parameter signs of the normalization layer to be consistent with the watermark sign, thereby achieving watermark embedding. Experimental results show that our FL model watermarking scheme excels in malicious client detection, leakage tracing, and copyright verification, with minimal impact on model performance, able to resist various attacks, such as fine-tuning, pruning, and quantization.

With the increasing demand for copyright protection of high-precision and sensitive vector data in Geographical Information System (GIS), research on lossless watermarking has attracted more and more attention. In this paper, a new lossless watermarking method based on line pairs is proposed. Firstly, the points and polygons are unified into the polylines, and then every two adjacent polylines are combined into a line pair. Besides, the storage direction and the interior angle of the line pairs are analyzed. Secondly, the watermark bit is determined by the interior angle of each line pair. Then, the watermark information is embedded by judging whether the storage direction of each line pair and the watermark bit is the same. Finally, experimental results verify that the proposed method works effectively for the data of the points, polylines and polygons, and has good invisibility without damaging the vector data. In addition, compared with the existing algorithm, the proposed method achieves higher robustness against geometric attacks, such as translating, rotating, and scaling the vector data.