Chaotic System Zero Watermark for Vector Geospatial Data
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Solution Overview
Problem
Existing commutative encryption and watermarking methods for vector geographic data lack robustness against geometric, projection, and reordering attacks, and have limited watermark capacity, making them unsuitable for high-precision data and vulnerable to conventional attacks.
Innovation Solution
A method combining chaotic systems with zero watermarks and scrambling encryption, which generates a zero watermark image by performing XOR operations on feature matrices constructed from vertex coordinate parity and binary copyright matrices, ensuring high-precision data integrity and enhanced robustness against attacks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If embedded watermarking is used for vector geographic data, then copyright protection is achieved, but data precision is degraded and robustness to geometric attacks is insufficient
Solution Approach 1:
The patent extracts stable topological features (vertex coordinate numbers, edge connectivity) from the vector geographic data to construct feature matrices. These extracted features are used to generate watermarks through XOR operations, separating the watermarking process from the original data structure. This extraction approach ensures that watermarks are derived from invariant properties rather than embedding operations that would degrade precision.
Solution Approach 2:
The patent creates zero watermarks by copying feature matrices and performing XOR operations between the feature matrix and binary copyright images. The zero watermark is generated as a copy of the feature matrix modified by the copyright information, allowing for robust watermark embedding without directly modifying the original vector geographic data, thus preserving data precision while achieving copyright protection.
2Quantity of substance
If conventional watermarking methods are used, then copyright protection is provided, but watermark capacity is limited and robustness to projection attacks is insufficient
Solution Approach 1:
The patent transitions from traditional spatial embedding to a topological dimension by using vertex coordinate numbers and edge connectivity relationships. The feature matrix is constructed by arranging topological features in a structured format, and watermarks are embedded in this topological dimension through XOR operations. This dimensional transformation enables higher watermark capacity while maintaining robustness against geometric and projection attacks that would not affect topological invariants.
Solution Approach 2:
The patent combines multiple watermarked feature matrices into a composite structure. The zero watermark is generated by XORing the feature matrix with the binary copyright image, creating a composite watermark structure that integrates copyright information with topological features. This composite approach increases watermark capacity while the use of multiple invariant features ensures robustness against various attacks.
3Reliability
If encryption is applied to vector geographic data, then security in transmission is ensured, but data accuracy is affected by calculation and storage mechanisms
Solution Approach 1:
The patent replaces traditional numerical encryption methods with a cryptographic system based on chaotic sequences and XOR operations. The encryption key is generated through chaotic system dynamics, and the encryption process uses bitwise XOR operations on the feature matrix. This substitution of mechanical calculation-based encryption with cryptographic operations ensures security while avoiding the precision degradation associated with traditional numerical encryption and storage mechanisms.
Data Source
AI summary
Disclosed in the present disclosure is a commutative encryption and watermarking method based on a chaotic system and a zero watermark for vector geospatial data. According to the method, firstly, the vector geospatial data are scrambled and encrypted by using chaotic sequences generated by a composite chaotic system. Then, vector geospatial elements are randomly combined in pairs. A feature matrix is constructed according to the number of vertex coordinates of the vector geospatial elements in combinations, and the parity of the number. Finally, an XOR operation is performed on the feature matrix and the watermark image to construct a zero watermark image, and the zero watermark is constructed through invariant features of the vector geospatial data.