Zero watermarking does not alter the original information contained in vector map data and provides perfect imperceptibility. The use of zero watermarking for data copyright protection has become a significant trend in digital watermarking research. However, zero watermarking encounters tremendous obstacles to its development and application because of its requirement to store copyright information with a third party and its difficulty in confirming copyright ownership. Aiming at the shortcomings of the existing zero watermarking technology, this paper proposes a new zero watermarking construction method based on the angular features of vector data that store the zero watermarking and copyright information on the blockchain after an XOR operation. When the watermark is being extracted, the copyright information can be extracted with the XOR operation to obtain the information stored on the blockchain. Experimental results show that the combination of zero watermarking and blockchain proposed in this paper gives full play to the advantages of the two technologies and protects the copyright of data in a lossless fashion. Compared with the traditional zero watermarking algorithms, the proposed zero watermarking algorithm exhibits stronger robustness. Moreover, the proposed data copyright protection framework with a combination of zero watermarking and blockchain can also be applied to other data types, such as images, audio, video, and remote sensing images.

Existing zero-watermarking algorithms for remote sensing images heavily rely on traditional feature extraction techniques, which are vulnerable to targeted attacks and lack discriminability for images captured by different sensors or at different time periods in the same geographical area. To address these limitations, this article proposes a novel watermarking algorithm based on blockchain and stacked denoising autoencoder (SDAE) to achieve lossless copyright protection for high-resolution remote sensing images. The algorithm utilizes SDAE to extract deep and robust features from local square feature regions for watermark construction. Moreover, the algorithm incorporates a watermark registration scheme designed with Hyperledger Fabric and InterPlanetary file system to ensure secure and trustworthy registration of watermarks and associated parameter information, enhancing the algorithm's uniqueness. Experimental results demonstrate the effectiveness of the proposed algorithm against various watermark attacks and its high discriminability for similar images. This algorithm holds significant potential for wide-ranging applications in the field of lossless copyright protection for high resolution remote sensing images (HRRS), effectively safeguarding the commercial interests of data providers.

The integrity of remote-sensing image data is susceptible to corruption during storage and transmission. Perceptual hashing is a non-destructive data integrity-protection technique suitable for high-accuracy requirements of remote-sensing image data. However, the existing remote-sensing image perceptual hash-authentication algorithms face security issues in storing and transmitting the original perceptual hash value. This paper proposes a remote-sensing image integrity authentication method based on blockchain and perceptual hash to address this problem. The proposed method comprises three parts: perceptual hash value generation, secure blockchain storage and transmission, and remote-sensing image integrity authentication. An NSCT-based perceptual hashing algorithm that considers the multi-band characteristics of remote-sensing images is proposed. A Perceptual Hash Secure Storage and Transmission Framework (PH-SSTF) is designed by combining Hyperledger Fabric and InterPlanetary File System (IPFS). The experimental results show that the method can effectively verify remote-sensing image integrity and tamper with the location. The perceptual hashing algorithm exhibits strong robustness and sensitivity. Meanwhile, the comparison results of data-tampering identification for multiple landscape types show that the algorithm has stronger stability and broader applicability compared with existing perceptual hash algorithms. Additionally, the proposed method provides secure storage, transmission, and privacy protection for the perceptual hash value.

In this letter, a new unambiguous technique based on the pseudo correlation function (PCF) is proposed for multiplexed binary offset carrier (MBOC) signal, which will be utilized in European Galileo system and Chinese BeiDou II system. The proposed technique firstly designs two local auxiliary signals based on the shape of the received MBOC signal. The synthesized correlation function is obtained by subtracting the squared cross-correlation function between the received MBOC signal and the local auxiliary signal from the squared cross-correlation function between the received MBOC signal and another local auxiliary signal. The simulation results demonstrate that the proposed technique can remove the ambiguity completely with slightly degradation in tracking accuracy.

For ensuring the integrity of high-resolution remote sensing (HRRS) images, the perceptual hash method offers a dual advantage: it preserves the non-destructive nature of the original image while also ensuring robustness to content-preserving operations. However, current deep learning based HRRS image hashing methods for integrity authentication are notably limited as they terminate at the feature extraction stage and fail to achieve an end-to-end construction from image to hash value. Consequently, there is a looming risk of uncontrollability and unexpected events. To overcome this problem, this paper proposes A Deep Subject-Sensitive Hashing Network (DSSHN), presenting a unified network for end-to-end feature extraction and hash construction. Improved Convolutional Block Attention Module (I-CBAM) helps the network to focus more on subject-sensitive features. A targeted training scheme ensures perceptual hash robustness. Experimental results reveal that the algorithm achieves the best tampering detection performance, with top AUC (0.994) and leading precision and recall rates.