Blind Digital Watermarking Algorithm against Projection Transformation for Vector Geographic Data

Zhou, Qian-Mei; Ren, Na; Zhu, Chenchen; Zhu, A‐Xing

doi:10.3390/ijgi9110692

Cited by 15 publications

(10 citation statements)

References 30 publications

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“…Table 12 reflects the results of watermark extraction, it is obvious that the watermark can be extracted successfully and that the NC values are always 1, which shows that the extracted watermark is not affected by data format conversion. rithm is more robust in resisting vertex addition attack and vertex deletion attac shown in Figure 11c-f, the proposed algorithm has the same robustness as the exi methods [2,13,24] in resisting data format conversion, scaling and translation att whereas its robustness in resisting rotation attack is not as good as the existing met [2,13,24]. The above results show that the proposed algorithm is resistant to vertex att scaling and translation attacks, data format conversion attack.…”

Section: ) Watermark Robustnessmentioning

confidence: 78%

“…Figure 11a-b reflect that the proposed algorithm is more robust in resisting vertex addition attack and vertex deletion attack. As shown in Figure 11c-f, the proposed algorithm has the same robustness as the existing methods [2,13,24] in resisting data format conversion, scaling and translation attacks, whereas its robustness in resisting rotation attack is not as good as the existing methods [2,13,24].…”

Section: Attack Type Attack Strength Ncmentioning

confidence: 94%

“…To further highlight the better performance of our method, the Railways map was used to implement the comparison with the existing methods [2,13,24]. We compared their robustness in vertex attacks, geometric attacks and data format conversion attack.…”

Section: Attack Type Attack Strength Ncmentioning

confidence: 99%

“…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%

See 3 more Smart Citations

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

Zhang

Wang

et al. 2021

IJGI

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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.

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Section: ) Watermark Robustnessmentioning

confidence: 78%

Section: Attack Type Attack Strength Ncmentioning

confidence: 94%

Section: Attack Type Attack Strength Ncmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Zhang

Wang

et al. 2021

IJGI

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“…Projection transformation is a process of projecting spatial data from one coordinate system to another [33]. Since the experimental data in this paper have a projected coordinate system, two types of projection transformation attacks are set.…”

Section: Projection Transformation Attacksmentioning

confidence: 99%

Zero Watermarking Algorithm for Vector Geographic Data Based on the Number of Neighboring Features

et al. 2021

Self Cite

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Zero watermarking is an important part of copyright protection of vector geographic data. However, how to improve the robustness of zero watermarking is still a critical challenge, especially in resisting attacks with significant distortion. We proposed a zero watermarking method for vector geographic data based on the number of neighboring features. The method makes full use of spatial characteristics of vector geographic data, including topological characteristics and statistical characteristics. First, the number of first-order neighboring features (NFNF) and the number of second-order neighboring features (NSNF) of every feature in vector geographic data are counted. Then, the watermark bit is determined by the NFNF value, and the watermark index is determined by the NSNF value. Finally, combine the watermark bits and the watermark indices to construct a watermark. Experiments verify the theoretical achievements and good robustness of this method. Simulation results also demonstrate that the normalized coefficient of the method is always kept at 1.00 under the attacks that distort data significantly, which has the superior performance in comparison to other methods.

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

Wang

Zhang

et al. 2022

Earth Sci Inform

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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.

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Blind Digital Watermarking Algorithm against Projection Transformation for Vector Geographic Data

Cited by 15 publications

References 30 publications

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

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

Zero Watermarking Algorithm for Vector Geographic Data Based on the Number of Neighboring Features

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

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