Dual Zero-Watermarking Scheme for Two-Dimensional Vector Map Based on Delaunay Triangle Mesh and Singular Value Decomposition

Abstract

Digital watermarking is important for the copyright protection of electronic data, but embedding watermarks into vector maps could easily lead to changes in map precision. Zero-watermarking, a method that does not embed watermarks into maps, could avoid altering vector maps but often lack of robustness. This study proposes a dual zero-watermarking scheme that improves watermark robustness for two-dimensional (2D) vector maps. The proposed scheme first extracts the feature vertices and non-feature vertices of the vector map with the Douglas-Peucker algorithm and subsequently constructs the Delaunay Triangulation Mesh (DTM) to form a topological feature sequence of feature vertices as well as the Singular Value Decomposition (SVD) matrix to form intrinsic feature sequence of non-feature vertices. Next, zero-watermarks are obtained by executing exclusive disjunction (XOR) with the encrypted watermark image under the Arnold scramble algorithm. The experimental results show that the scheme that synthesizes both the feature and non-feature information improves the watermark capacity. Making use of complementary information between feature and non-feature vertices considerably improves the overall robustness of the watermarking scheme. The proposed dual zero-watermarking scheme combines the advantages of individual watermarking schemes and is robust against such attacks as geometric attacks, vertex attacks and object attacks.

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

“…The second type is the method based on spatial characteristics [8,9,[18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. It quantifies the coordinates of vector geographic data to construct the zero watermark.…”

“…Another is to use the coordinate indirectly. For example, scholars employ the angle [8,19,20,[29][30][31][32], distance [25,26], distance ratio [22,27], and topology [28] to construct a watermark, respectively. Compared with attribute characteristics, spatial characteristics are the basis of vector geographic data.…”

Commutative Encryption and Watermarking Algorithm Based on Feature Invariants for Secure Vector Map

“…Only the authorized customer who has the right key can recover the data correctly, but it cannot achieve the copyright trace for data in plaintext or ciphertext. Compared with the encryption technique, the watermarking technique embeds the invisible information into the data, which can be extracted and used to authenticate data's ownership or identification, but it cannot ensure the security in data transmission [11]- [18]. Therefore, it is possible to overcome the limitation of a single technique by combining both the techniques of encryption and watermarking together to protect both confidentiality and ownership/ identification [19]- [21].…”

Blind Digital Watermarking Algorithm against Projection Transformation for Vector Geographic Data

“…The second category of studies is the blind watermarking scheme, in which the original data or related information are not required to perform the extraction of the watermark. Watermark embedding and extraction are usually achieved by finding the features which are invariant (feature invariants hereafter) to the projection transformation [27][28][29][30][31]. For example, Yang and Hou [28] presented a method based on a kind of polyline transformation, which converts the polyline into a representation of distance and angle.…”