CN106204411A - Vector settlement place Zero watermarking method based on not bending moment and Hilbert code - Google Patents

Abstract

The invention discloses a zero watermark method for vector residential data. In order to enhance the clipping resistance of the zero watermark, the present invention uses the Hilbert permutation code to divide the data into blocks so as to construct multiple zero watermarks. The construction method of each zero watermark is as follows: firstly, use the Arnold technology to scramble the original watermark image; then, calculate the vector invariant moment set of the residential map and map it into a binary matrix; finally, the scrambled watermark The XOR operation is performed on the image and the binary matrix mapped by the invariant moment set to complete the construction of the zero watermark. Experimental results show that the algorithm can effectively resist common geometric attacks and clipping attacks, and it is also robust to the addition and deletion attacks of residential objects.

Description

Zero Watermarking Method of Vector Residential Areas Based on Invariant Moments and Hilbert Codes

technical field

The invention belongs to the field of cartography and geographic information science and technology, and relates to a zero watermark method based on invariant moments and Hilbert codes for vector settlement data.

Background technique

The production cost of vector map data is high, it is widely used in social and economic activities, and is closely related to national security, so the research on its copyright protection is of great significance. On a map, whether it is a general map or a thematic map, the settlement is an important and indispensable map element in the map expression. Therefore, copyright protection of residential data is particularly important in the process of map dissemination.

Digital watermarking technology plays an important role in the application of data copyright protection and data authenticity detection. It is a new technology that can realize information hidden domain tracking in an open network environment. At the same time, due to the particularity of vector geospatial data in terms of data structure, storage method, form of expression and application environment, digital watermarking technology plays an important role in protecting the security of vector maps.

Academia has proposed many watermarking methods for vector map data. However, the existing watermarking methods for vector surface data cannot be directly used for residential data. The shape of the residential area is relatively regular, the number of vertices is small, and the topological relationship is separation relationship. The existing watermarking method for surface data belongs to the embedded method, and its research objects have complex shapes, many vertices, and there are common edges between the surface elements, which belongs to continuous surface data. This type of method regards the vector surface data as a "point cloud" collection, and the watermark information is directly hidden in the vertices or their frequency domain coefficients. This kind of idea of using vertices as embeddings ignores the shape and object of the research object. Considering the spatial relationship between objects may lead to serious shape deformation of the embedded watermarked object, conflict or inconsistency in the topological relationship between objects. If this embedded watermarking method is used for residential data, it will destroy the rectangular characteristics of the residential area, and it will also cause two residential areas to overlap each other. Therefore, there is an urgent need to study watermarking methods suitable for residential data in order to improve the vector data copyright protection technology.

On the 3rd Information Hiding Symposium, Hot Spring first proposed the concept of zero watermark. Zero watermark technology is to use the important characteristics of the original data to construct the watermark without any modification to the original data. In view of this, the present invention uses the zero-watermark technology to solve the problem of piracy of residential data, ensuring that the precision and topological relationship of the residential data remain unchanged.

Contents of the invention

Aiming at the deficiency that the existing embedded watermark method cannot be directly applied to vector residential data, the invention proposes a zero watermark method for residential data. The zero watermark technology makes up for the defects that the traditional embedded watermark method will cause serious shape deformation of the embedded object, conflict or inconsistency of the topological relationship between objects. At the same time, experiments show that the method can effectively resist common geometric attacks and clipping attacks, and it is also robust to the addition and deletion attacks of residential objects.

The present invention includes zero watermark construction, zero watermark detection and optimization strategies for adding and deleting attacks.

Zero watermark construction refers to the fusion of the feature quantity extracted from the original residential data and the encrypted watermark information of the original watermark image to construct the watermark image. The steps are as follows: Use the Hilbert permutation code to block the data in order to construct multiple zero watermarks. The construction steps of each zero watermark are as follows: firstly, use the Arnold technology to scramble the original watermark image; then, calculate the vector invariant moment set of the residential area graphics and map it into a binary matrix; finally, the scrambled watermark The XOR operation is performed on the image and the binary matrix mapped by the invariant moment set to complete the construction of the zero watermark.

Zero watermark detection is to use the registered zero watermark and original data feature to restore the original watermark image. The steps are as follows, according to the block rules in the construction process, the data are divided into the same blocks, and the vector invariant moment set of the residential area figure is calculated and mapped into a binary matrix; the binary matrix is combined with the registered zero-watermark image A scrambled watermark image is obtained by XOR operation, and the original watermark image is restored by descrambling the watermark image.

The above extraction method simply implements the reverse process of the construction process to complete the zero watermark extraction, which may be less resistant to the addition and deletion of residential objects. In order to make up for the shortcomings of the extraction method, the present invention proposes an optimization strategy for adding and deleting attacks , that is, before watermark detection, optimize the residential data subjected to addition and deletion operations and then extract the zero watermark. Due to the addition or deletion of residential areas, some feature quantities will be redundant or missing, resulting in serious dislocation and XOR operations between feature information and watermark information. In the present invention, by locating the position of the feature quantity of the tampered data in the construction sequence, and then modifying the value at the corresponding position in the binary sequence, the binary code corresponding to the invariant moment of the added object is eliminated or the equal length is filled for the deleted object. Binary code sequence, so as to eliminate the impact of misalignment operation on watermark detection.

The method of the invention is advanced and scientific, and at the same time of completing the copyright mark, it ensures that the precision of the residential area data and the topological relationship remain unchanged. Experiments show that this method can effectively resist common geometric attacks (translation, scaling, rotation) and cropping attacks, and it is also robust to addition and deletion attacks. It is also a natural blind watermarking method with comparative Good value for use.

Description of drawings

Figure 1 is a flow chart of zero watermark construction

Figure 2(a) is the original watermarked image

Figure 2(b) is the image of (a) after Arnold scrambled once

Figure 2(c) is the image of (a) after Arnold scrambled 15 times

Figure 2(d) is the image of (a) after Arnold scrambled 24 times

Figure 3 is a visual display of the original settlement data

Figure 4 is the rendering of 1/4 clipping of residential area data

Figure 5 shows the zero watermark detection results under different levels of tailoring attacks

Figure 6 is the zero watermark detection results under the addition and deletion attacks

detailed description

In order to describe the technical content, structural features, objectives and effects of the present invention in detail, the following will be described in detail in conjunction with specific embodiments.

The implementation steps of the present invention can be summarized into two parts: zero-watermark construction and zero-watermark detection and optimization strategy of adding and deleting attacks. Each implementation step is further described below.

Step 1: Sort the objects of residential places according to their values, and use the formula (8) to calculate the 7 invariant moments of each residential place in this order to generate an invariant moment set.

Step 2: Express the positive numbers in the invariant moment set with "1" and the negative numbers with "0" to generate a binary matrix.

Step 3: Read the matrix value of the original watermark image to get the matrix

P×Q=N is the image matrix size. The original image is scrambled and transformed, and according to the operation rules of formula (1), the binary feature sequence T and the scrambled watermark image matrix are XORed to obtain a zero watermark image:

(1).

Zero watermark detection is the reverse process of zero watermark construction, and the specific steps are as follows.

Step 1: Sort the objects of residential places according to their values, and use the formula (8) to calculate the 7 invariant moments of each residential place in this order to generate an invariant moment set.

Step 2: Express the positive numbers in the invariant moment set with "1" and the negative numbers with "0", and generate a binary matrix.

Step 3: XOR the binary matrix with the zero-watermarked image to obtain a scrambled watermarked image, and use the periodicity of the Arnold transformation to descramble the watermarked image to obtain a watermarked image containing copyright information.

The specific implementation steps of the optimization strategy for adding and deleting attacks are as follows: In the present invention, residential areas are used as the research object, and each residential area has 7 invariant moments, and each invariant moment occupies a watermark bit, which means that a residential area If it is deleted or added, 7 watermark bits are missing or redundant, which seriously causes the dislocation XOR operation of feature information and watermark information. Therefore, it is first necessary to determine whether the attacker has added or deleted the data. Then discuss in two cases: if it is judged to be an increase attack, first find the position where the added feature is located, and then remove its corresponding binarized value; if it is a deletion attack, find the location where the missing feature is located position, and then fill the value 0 at the corresponding position of the binary sequence, and its length is 7 times the number of deleted data. Finally, use the optimized binary matrix to do XOR operation with the zero watermark image.

To sum up, the present invention effectively solves the copyright identification problem of the vector residential data, and at the same time ensures the accuracy of the residential data and the consistency of the topological relationship. Moreover, it has good robustness against attacks such as addition, deletion, and cropping of residential objects. It is a natural blind watermarking method and has good use value.

Claims (1)

1. vector settlement place Zero watermarking method based on not bending moment and Hilbert code, its feature comprises the following steps:

(1), before zero watermarking structure, utilize Hilbert permutation code to deblocking to construct multiple zero watermarking；

(2) structure of zero watermarking: zero watermarking structure refers to utilize characteristic quantity and the original watermark of original settlement place extracting data Watermark information fusion constructs watermarking images after image encryption process, step is as follows, first, utilizes Arnold technology scramble original Watermarking images；Then, calculate the vector not bending moment collection of settlement place figure and map it onto a two values matrix；Finally, will put Watermarking images after unrest carries out XOR with the two values matrix that bending moment collection is not mapped, and completes zero watermarking structure；

(3) extraction of zero watermarking: according to the piecemeal rule in construction process, data are carried out identical piecemeal, calculate resident's map The vector of shape not bending moment collection also maps it onto a two values matrix；This two values matrix is carried out with registered zero watermarking image XOR obtains the watermarking images of a scramble, and through anti-scramble transformation, this watermarking images is recovered original watermark image；

(4) increase, delete attack optimisation strategy: if be judged as increasing attack, first find the position at the characteristic quantity place of increase, then Reject the binary value that it is corresponding；If delete attack, the position at the characteristic quantity place of disappearance to be found, then its two Numerical value 0 is filled up in value sequence corresponding position, 7 times of its a length of deleted data amount check；Finally by the two-value through optimizing Matrix and zero watermarking image do XOR.