Combinational spatial and frequency domains watermarking for 2D vector maps

Zhang, Liming; Yan, Haowen; Zhu, Rui; Du, Ping

doi:10.1007/s11042-020-09573-3

Cited by 15 publications

(7 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Step 4 The numbers of any two objects' vertices are operated and indexed with the length of watermark, as shown in Equation (1). Then the index sequence {} i Index is obtained, expressed by…”

Section: Figure 3 the Basic Idea Of Proposed Methodsmentioning

confidence: 99%

“…In order to solve the three problems in Section 2, the following three aspects are proposed to design the zero-watermarking algorithm: (1) The number of object vertices and the relative storage order are used as two feature invariants for the projection transformation. (2) The numbers of two objects vertices are used to determine the watermark index.…”

Section: Basic Ideamentioning

confidence: 99%

“…With the rapid development of geographic information science, vector geographic data plays an important role in the national economy, urban national security, and environmental protection [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A zero-watermarking algorithm for vector geographic data based on feature invariants

Wang

Zhang

et al. 2022

Earth Sci Inform

Self Cite

View full text Add to dashboard Cite

Most researches on zero-watermarking algorithms for vector geographic data focus on improving the robustness against geometrical attacks, compression attacks and object attacks. However, there are few zero-watermarking algorithms against projection transformation. We proposed a zerowatermarking algorithm for vector geographic data based on feature invariants. After any projection transformation of vector geographic data, the number of vertices and relative storage order of objects does not change. Therefore, the number of vertices and relative storage order of objects can be considered as the feature invariants. Firstly, according to relative storage order of objects, the watermark bit is determined by comparing the number of vertices between any two objects. Secondly, the watermark index is calculated by the number of vertices of two objects. Then, a feature matrix is constructed combining the watermark bit and the watermark index. Finally, the XOR operation is performed between the feature matrix and the scrambled watermark image to generate the zerowatermark image. The experiments show that the watermark can be detected from the vector geographic data after any projection transformation. And this algorithm can effectively against geometrical attacks, object attacks and precision reduction attacks, showing superior performance compared with previous algorithms.

show abstract

Section: Figure 3 the Basic Idea Of Proposed Methodsmentioning

confidence: 99%

Section: Basic Ideamentioning

confidence: 99%

See 1 more Smart Citation

A zero-watermarking algorithm for vector geographic data based on feature invariants

Wang

Zhang

et al. 2022

Earth Sci Inform

Self Cite

View full text Add to dashboard Cite

show abstract

“…The above three algorithms all combine general watermarking and zero watermarking or reversible watermarking to embed two watermarks, and the watermark capacity is limited. In [31,32], two watermarks were embedded in the spatial domain and the frequency domain. In [33], four kinds of multiple watermarking algorithms were designed.…”

Section: Introductionmentioning

confidence: 99%

Multiple Watermarking Algorithms for Vector Geographic Data Based on Multiple Quantization Index Modulation

Wang,

Yang,

Ding

2023

Applied Sciences

View full text Add to dashboard Cite

Multiple digital watermarking is an important and challenging task in geographic information science and data security. Vector geographic data are a basic data format for digital geographic data storage, and the security protection of these data involves copyright protection and tracking. As part of the solution, existing digital watermarking algorithms have made contributions to vector geographic data protection. However, when vector geographic data flow through multiple units, they need to be marked to ensure that the original data are not destroyed during data processing. Existing single or multiple data watermarking algorithms often fail in the presence of data processing because the new watermarks overlay the old ones. Consequently, a multiple digital watermarking algorithm based on multiple QIM (quantization index modulation) is proposed. First, based on traditional quantization index modulation (QIM), a multiple QIM is proposed. Unlike traditional QIM, in multiple QIM, the process of quantization is executed multiple times depending on the number of watermarks. Then, the vertices are quantized into different quantization intervals according to the multiple QIM. Finally, multiple watermarks are embedded into different quantization intervals to reduce the interference among multiple watermarks, and the original watermarks are not needed in the process of watermark detection. We then conducted experiments to test the multiple watermark method’s robustness and capacity, with an emphasis on datasets with a lower data volume. The experimental results show that the proposed algorithm achieves good performance in terms of its robustness against common issues, such as vertices addition, data simplification, data cropping, and feature deletion; this holds true for both normal and small amounts of data. Additionally, it has a high multiple watermark capacity.

show abstract

“…Vector maps are one of the most important geospatial data [1,2], which play a vital role in economic development and national security [3,4]. Currently, vector maps have been widely used in navigation, cadastral management, urban planning and many other fields [5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Invariant Based Commutative Encryption and Watermarking Algorithm for Vector Maps

Zhang

Wang

et al. 2021

IJGI

Self Cite

View full text Add to dashboard Cite

Commutative encryption and watermarking (CEW) is an emerging method that combines encryption technology with digital watermarking technology. It has the dual capability of secure transmission and copyright protection. However, the existing CEW methods for vector maps have good robustness in resisting geometric attacks but poor resistance to vertex attacks (e.g., addition, deletion, etc.). To solve this problem, here we propose a novel invariant-based CEW algorithm for vector maps, which consists of permutation-based encryption scheme and coordinates-based watermarking scheme. In the encryption scheme, the encryption key is generated via the Gaussian distribution method combined with the SHA-512 hash method; then, the double random position permutation strategy is applied to the vector map encryption. In watermarking embedding scheme, the original watermark image is scrambled via logistic chaotic encryption before embedding, and the coordinates of all the vertices are normalized. Then, the scrambled watermark image is embedded into the normalized coordinates. Results show that: proposed method is more robust to conventional attacks (e.g., vertex addition and deletion, reordering and data format conversion) and geometric attacks (e.g., scaling and translation). In addition, compared with the existing CEW methods for vector maps, the proposed method has higher security and stronger robustness against vertex attacks.

show abstract

Combinational spatial and frequency domains watermarking for 2D vector maps

Cited by 15 publications

References 19 publications

A zero-watermarking algorithm for vector geographic data based on feature invariants

A zero-watermarking algorithm for vector geographic data based on feature invariants

Multiple Watermarking Algorithms for Vector Geographic Data Based on Multiple Quantization Index Modulation

A Novel Invariant Based Commutative Encryption and Watermarking Algorithm for Vector Maps

Contact Info

Product

Resources

About