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

Vector settlement place Zero watermarking method based on not bending moment and Hilbert code Download PDF

Info

Publication number
CN106204411A
CN106204411A CN201610554609.2A CN201610554609A CN106204411A CN 106204411 A CN106204411 A CN 106204411A CN 201610554609 A CN201610554609 A CN 201610554609A CN 106204411 A CN106204411 A CN 106204411A
Authority
CN
China
Prior art keywords
zero
watermark
watermarking
vector
image
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN201610554609.2A
Other languages
Chinese (zh)
Other versions
CN106204411B (en
Inventor
姜晓琴
闫浩文
张黎明
田坤瑞
魏征
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Lanzhou Jiaotong University
Original Assignee
Lanzhou Jiaotong University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Lanzhou Jiaotong University filed Critical Lanzhou Jiaotong University
Priority to CN201610554609.2A priority Critical patent/CN106204411B/en
Publication of CN106204411A publication Critical patent/CN106204411A/en
Application granted granted Critical
Publication of CN106204411B publication Critical patent/CN106204411B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING OR COUNTING
    • G06TIMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T1/00General purpose image data processing
    • G06T1/0021Image watermarking
    • G06T1/005Robust watermarking, e.g. average attack or collusion attack resistant
    • G06T1/0064Geometric transfor invariant watermarking, e.g. affine transform invariant

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Editing Of Facsimile Originals (AREA)
  • Image Processing (AREA)

Abstract

本发明公开了一种针对矢量居民地数据的零水印方法。为增强零水印的抗裁剪能力,本发明利用Hilbert排列码对数据分块以便构造多个零水印。每块零水印的构造方法为:首先,利用Arnold技术置乱原始水印图像;然后,计算居民地图形的矢量不变矩集并将其映射成一个二值矩阵;最后,将置乱后的水印图像与不变矩集所映射的二值矩阵进行异或运算,完成零水印构造。实验结果表明,该算法能有效抵抗常见的几何攻击、裁剪攻击,同时对于居民地对象的增、删攻击也有具有较强的鲁棒性。

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

基于不变矩和Hilbert码的矢量居民地零水印方法Zero Watermarking Method of Vector Residential Areas Based on Invariant Moments and Hilbert Codes

技术领域technical field

本本发明属于地图学与地理信息科学技术领域,涉及一种针对矢量居民地数据的基于不变矩和Hilbert码的零水印方法。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.

背景技术Background technique

矢量地图数据生产成本高昂,在社会经济活动中应用广泛,并且与国家安全息息相关,因此对其版权保护问题的研究意义重大。在地图上,不管是普通地图还是专题地图,居民地是地图表达中重要的、不可或缺的地图要素。因此,居民地数据版权保护在地图传播过程中显得尤为重要。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.

数字水印技术在数据版权保护和数据真伪检测的应用中发挥着重要作用,是一种可在开放的网络环境下实现信息隐藏域跟踪的新技术。同时由于矢量地理空间数据在数据结构、存储方式、表现形式和应用环境等方面的特殊性,使得数字水印技术在保护矢量地图安全中发挥重要作用。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.

学术界提出了不少针对矢量地图数据的水印方法,然而,目前已有的针对矢量面数据的水印方法不能直接用于居民地数据。居民地形状较为规整,顶点个数少,且拓扑关系均为相离关系。现有的针对面数据的水印方法属于嵌入性方法,其研究对象形状复杂,顶点繁多,各面要素间具有公共边,属于连续面数据。这类方法把矢量面数据看成是“点云”集合,水印信息直接隐藏在顶点中或它们的频率域系数中,这种以顶点为嵌入体的思想,忽略了对研究对象的形状、对象间空间关系的考虑,可能导致嵌入水印后的对象产生严重形状变形、对象间拓扑关系的冲突或不一致性。如果将这种嵌入型水印方法用于居民地数据,会破坏居民地的直角化特征,也会导致两个居民地相互压盖。因此,迫切需要研究适用于居民地数据的水印方法,以完善矢量数据版权保护技术。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.

第三届信息隐藏学术研讨会上,温泉最先提出零水印概念。零水印技术就是利用原数据的重要特征来构造水印,而不对原始数据做任何修改。鉴于此,本发明利用零水印技术来解决居民地数据盗版问题,保证居民地数据精度和拓扑关系不变。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.

发明内容Contents of the invention

本发明针对现有嵌入型水印方法不能直接应用于矢量居民地数据的不足,提出一种针对居民地数据的零水印方法。零水印技术弥补了传统嵌入型水印方法会导致被嵌入对象产生严重形状变形、对象间拓扑关系的冲突或不一致性的缺陷。同时,实验表明该方法能有效抵抗常见的几何攻击、裁剪攻击,同时对于居民地对象的增、删攻击也有具有较强的鲁棒性。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.

本发明包括零水印的构造、零水印检测以及增、删攻击的优化策略。The present invention includes zero watermark construction, zero watermark detection and optimization strategies for adding and deleting attacks.

零水印构造是指利用原始居民地数据中提取的特征量与原始水印图像加密处理后的水印信息融合构造水印图像。步骤如下:利用Hilbert排列码对数据分块以便构造多个零水印。每块零水印的构造步骤为:首先,利用Arnold技术置乱原始水印图像;然后,计算居民地图形的矢量不变矩集并将其映射成一个二值矩阵;最后,将置乱后的水印图像与不变矩集所映射的二值矩阵进行异或运算,完成零水印构造。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.

零水印检测是利用已注册过的零水印与原始数据特征量,恢复原始水印图像。步骤如下,按照构造过程中的分块规则对数据进行相同分块,计算居民地图形的矢量不变矩集并将其映射成一个二值矩阵;将该二值矩阵与已注册的零水印图像进行异或运算得到一个置乱的水印图像,将该水印图像经过反置乱变换恢复出原始水印图像。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.

以上提取方法简单地实施构造过程的逆过程完成零水印提取,可能对居民地对象增、删攻击的抵抗性较弱,为了弥补该提取方法的不足,本发明提出一个增、删攻击的优化策略,即在水印检测前对遭受增、删操作的居民地数据进行优化处理再提取零水印。由于居民地的增加或删除会导致部分特征量富余或缺失,从而造成特征信息与水印信息严重的错位异或运算。本发明通过定位被篡改数据的特征量在构造序列中的位置,然后修改二值序列中相应位置处的值,剔除被增加对象的不变矩所对应的二值码或者为删除对象填补等长二值码序列,从而消除因错位运算对水印检测造成的影响。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.

本发明方法先进、科学,在完成版权标识的同时,保证了居民地数据精度和拓扑关系不变。通过实验表明,该方法能够有效抵抗常见的几何攻击(平移、缩放、旋转)、裁剪攻击,对增、删攻击也具有较好的鲁棒性,并且是一种天然的盲水印方法,具有较好的使用价值。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.

附图说明Description of drawings

图1 是零水印构造流程图Figure 1 is a flow chart of zero watermark construction

图2(a) 是原始水印图像Figure 2(a) is the original watermarked image

图2(b) 是(a)图 Arnold置乱1次后图像Figure 2(b) is the image of (a) after Arnold scrambled once

图2(c) 是(a)图 Arnold置乱15次后图像Figure 2(c) is the image of (a) after Arnold scrambled 15 times

图2(d) 是(a)图 Arnold置乱24次后图像Figure 2(d) is the image of (a) after Arnold scrambled 24 times

图3 是原始居民地数据可视化显示Figure 3 is a visual display of the original settlement data

图4 是居民地数据裁剪1/4的效果图Figure 4 is the rendering of 1/4 clipping of residential area data

图5 是不同程度裁剪攻击下零水印检测结果Figure 5 shows the zero watermark detection results under different levels of tailoring attacks

图6 是增、删攻击下零水印检测结果Figure 6 is the zero watermark detection results under the addition and deletion attacks

具体实施方式detailed description

为了详细说明本发明的技术内容、构造特征、所实现的目的及所达到的效果,以下结合具体实施方式详细说明。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.

本发明的实施步骤可以概括为两个部分:零水印构造和零水印检测以及增、删攻击的优化策略。下面对各实施步骤进行进一步的阐述。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.

步骤一:将居民地对象按值进行排序,按此顺序利用公式(8)依次计算各居民地的7个不变矩,生成不变矩集。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.

步骤二:将不变矩集中的正数用“1”表示,负数用“0”表示,生成一个二值矩阵。 Step 2: Express the positive numbers in the invariant moment set with "1" and the negative numbers with "0" to generate a binary matrix.

步骤三:读取原始水印图像的矩阵值,得矩阵Step 3: Read the matrix value of the original watermark image to get the matrix

P×Q=N为图像矩阵大小。将原始图像置乱变换得到,根据公式(1)的运算规则,将二值特征序列T与置乱后水印图像矩阵诸位异或运算得到零水印图像: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)。 (1).

零水印检测是零水印构造的逆过程,具体步骤如下。Zero watermark detection is the reverse process of zero watermark construction, and the specific steps are as follows.

步骤一:将居民地对象按值进行排序,按此顺序利用公式(8)依次计算各居民地的7个不变矩,生成不变矩集。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.

步骤二:将不变矩集中的正数用“1”表示,负数用“0”表示,并生成一个二值矩阵。Step 2: Express the positive numbers in the invariant moment set with "1" and the negative numbers with "0", and generate a binary matrix.

步骤三:将二值矩阵与零水印图像做异或运算,得到一个置乱的水印图像,利用Arnold变换的周期性对该水印图像进行反置乱得到含版权信息的水印图像。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.

增、删攻击的优化策略具体实施步骤如下:在本发明中以居民地作为研究对象,每个居民地有7个不变矩,每个不变矩占据一个水印位,这意味着一个居民地被删除或增加,就有7个水印位缺失或多余,从而严重造成特征信息与水印信息的错位异或运算。因此,首先要判断,攻击者对数据做了增加还是删除操作。然后再分两种情况讨论:若判断为增加攻击时,先找到增加的特征量所在的位置,然后剔除它所对应的二值化值;若为删除攻击,则要找到缺失的特征量所在的位置,然后在其二值序列相应位置处填补数值0,其长度为所删除的数据个数的7倍。最后用经过优化的二值矩阵与零水印图像做异或运算。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.

综上所述,本发明有效解决了矢量居民地数据版权标识问题的同时,保证了居民地数据精度和拓扑关系一致性。并且对于居民地对象增加、删除、裁剪等攻击具有较好的鲁棒性,是一种天然的盲水印方法,具有较好的使用价值。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.
CN201610554609.2A 2016-07-15 2016-07-15 Vector settlement place Zero watermarking method based on not bending moment and Hilbert code Active CN106204411B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201610554609.2A CN106204411B (en) 2016-07-15 2016-07-15 Vector settlement place Zero watermarking method based on not bending moment and Hilbert code

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201610554609.2A CN106204411B (en) 2016-07-15 2016-07-15 Vector settlement place Zero watermarking method based on not bending moment and Hilbert code

Publications (2)

Publication Number Publication Date
CN106204411A true CN106204411A (en) 2016-12-07
CN106204411B CN106204411B (en) 2019-04-02

Family

ID=57475169

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201610554609.2A Active CN106204411B (en) 2016-07-15 2016-07-15 Vector settlement place Zero watermarking method based on not bending moment and Hilbert code

Country Status (1)

Country Link
CN (1) CN106204411B (en)

Cited By (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109903212A (en) * 2019-01-28 2019-06-18 郑州轻工业学院 An Image Encryption Method Based on H Geometric Fractal and Hilbert Curve
CN110570342A (en) * 2019-07-01 2019-12-13 齐鲁工业大学 Color medical image zero watermark construction method, system and detection method, system
CN111242825A (en) * 2019-12-17 2020-06-05 中国人民解放军海军大连舰艇学院 A zero-watermark method for ENC electronic nautical charts based on bathymetric features
CN112800395A (en) * 2021-01-27 2021-05-14 南京信息工程大学 A multi-image copyright authentication and verification method based on zero watermark technology
CN114897659A (en) * 2022-05-09 2022-08-12 南京师范大学 A zero-watermark generation algorithm for vector geographic data and a method for detecting zero-watermark information
CN116582246A (en) * 2023-06-16 2023-08-11 兰州交通大学 Cryptographic Watermarking Method for Vector Geospatial Data Exchange Based on Chaos and Zero Watermarking
CN116805069A (en) * 2023-08-18 2023-09-26 南京师范大学 Trajectory data zero watermark generation method, detection method, device and storage medium
CN116883226A (en) * 2023-07-21 2023-10-13 中国国土勘测规划院 NMF decomposition-based DEM zero watermark method, device and medium
CN117217973A (en) * 2023-09-14 2023-12-12 兰州交通大学 Three-dimensional point cloud data watermarking method using Mahalanobis distance and ISS feature points
CN119417688A (en) * 2025-01-07 2025-02-11 北京博道焦点科技有限公司 A high-precision map zero-watermark encryption method, device and electronic device

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080130883A1 (en) * 2006-04-26 2008-06-05 The Board Of Regents Of The University Of Texas System Methods and Systems for Digital Image Security
CN103366335A (en) * 2013-07-17 2013-10-23 兰州交通大学 Vector point spatial data full-blind watermarking method based on grid dividing
CN104091304A (en) * 2014-08-02 2014-10-08 兰州交通大学 Vector spatial data blind watermarking method based on feature point
CN104217390A (en) * 2014-07-15 2014-12-17 河南师范大学 Zero watermark method and device and watermark extraction method and device
CN105426710A (en) * 2015-11-12 2016-03-23 南京师范大学 Method for accurately authenticating vector geographic data based on spatial domain sequencing

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080130883A1 (en) * 2006-04-26 2008-06-05 The Board Of Regents Of The University Of Texas System Methods and Systems for Digital Image Security
CN103366335A (en) * 2013-07-17 2013-10-23 兰州交通大学 Vector point spatial data full-blind watermarking method based on grid dividing
CN104217390A (en) * 2014-07-15 2014-12-17 河南师范大学 Zero watermark method and device and watermark extraction method and device
CN104091304A (en) * 2014-08-02 2014-10-08 兰州交通大学 Vector spatial data blind watermarking method based on feature point
CN105426710A (en) * 2015-11-12 2016-03-23 南京师范大学 Method for accurately authenticating vector geographic data based on spatial domain sequencing

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
王永杰 等: "基于Hilbert空间排列码的海量空间数据划分算法研究", 《武汉大学学报 信息科学版》 *

Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109903212A (en) * 2019-01-28 2019-06-18 郑州轻工业学院 An Image Encryption Method Based on H Geometric Fractal and Hilbert Curve
CN110570342B (en) * 2019-07-01 2023-06-13 齐鲁工业大学 Color medical image zero watermark construction method and system and color medical image zero watermark detection method and system
CN110570342A (en) * 2019-07-01 2019-12-13 齐鲁工业大学 Color medical image zero watermark construction method, system and detection method, system
CN111242825A (en) * 2019-12-17 2020-06-05 中国人民解放军海军大连舰艇学院 A zero-watermark method for ENC electronic nautical charts based on bathymetric features
CN112800395A (en) * 2021-01-27 2021-05-14 南京信息工程大学 A multi-image copyright authentication and verification method based on zero watermark technology
CN112800395B (en) * 2021-01-27 2023-04-14 南京信息工程大学 A Multi-Image Copyright Authentication and Verification Method Based on Zero Watermark Technology
CN114897659B (en) * 2022-05-09 2023-12-29 南京师范大学 Vector geographic data zero watermark generation method and zero watermark information detection method
CN114897659A (en) * 2022-05-09 2022-08-12 南京师范大学 A zero-watermark generation algorithm for vector geographic data and a method for detecting zero-watermark information
CN116582246A (en) * 2023-06-16 2023-08-11 兰州交通大学 Cryptographic Watermarking Method for Vector Geospatial Data Exchange Based on Chaos and Zero Watermarking
CN116582246B (en) * 2023-06-16 2024-02-06 兰州交通大学 Vector geospatial data exchange cipher watermarking method based on chaos and zero watermarking
CN116883226A (en) * 2023-07-21 2023-10-13 中国国土勘测规划院 NMF decomposition-based DEM zero watermark method, device and medium
CN116883226B (en) * 2023-07-21 2024-01-02 中国国土勘测规划院 DEM zero watermark embedding and extracting method, device and medium
CN116805069A (en) * 2023-08-18 2023-09-26 南京师范大学 Trajectory data zero watermark generation method, detection method, device and storage medium
CN116805069B (en) * 2023-08-18 2023-11-03 南京师范大学 Track data zero watermark generation method, track data zero watermark detection device and storage medium
CN117217973A (en) * 2023-09-14 2023-12-12 兰州交通大学 Three-dimensional point cloud data watermarking method using Mahalanobis distance and ISS feature points
CN117217973B (en) * 2023-09-14 2024-06-07 兰州交通大学 Three-dimensional point cloud data watermarking method using mahalanobis distance and ISS feature points
CN119417688A (en) * 2025-01-07 2025-02-11 北京博道焦点科技有限公司 A high-precision map zero-watermark encryption method, device and electronic device

Also Published As

Publication number Publication date
CN106204411B (en) 2019-04-02

Similar Documents

Publication Publication Date Title
CN106204411A (en) Vector settlement place Zero watermarking method based on not bending moment and Hilbert code
Abubahia et al. Advancements in GIS map copyright protection schemes-a critical review
Wang et al. Reversible fragile watermarking for 2-D vector map authentication with localization
CN100461215C (en) A Robust Blind Watermark Embedding and Extraction Method Based on Map Data Rasterization
Yan et al. A normalization-based watermarking scheme for 2D vector map data
CN103310407B (en) Based on the vectorial geographical spatial data total blindness water mark method of QR code
Wang et al. Reversible fragile watermarking for locating tampered blocks in 2D vector maps
CN101458810A (en) Vector map watermark method based on object property characteristic
CN105488434B (en) A kind of map vector completeness certification method based on label
CN105761197A (en) Feature invariants based remote sensing image watermark method
Ren et al. Selective authentication algorithm based on semi-fragile watermarking for vector geographical data
CN103377457B (en) A kind of vector geographic data Hard Authentication vulnerable watermark method
CN110223213B (en) Vector space data digital fingerprint method for GD-PBIBD coding
CN103377455B (en) A kind of three-dimensional geographical pattern number word water mark method towards copyright protection service
Xun et al. A robust zero-watermarking algorithm for vector digital maps based on statistical characteristics
CN103955634A (en) Copyright protecting method based on digital watermark technology and aiming at tile remote sensing data
CN101576993B (en) Digital watermark embedding and extraction method for GIS vector data based on data mask
CN102800041A (en) Method for protecting integrity of digital vector map
Neyman et al. Reversible fragile watermarking based on difference expansion using manhattan distances for 2d vector map
Li et al. Study on copyright authentication of GIS vector data based on Zero-watermarking
CN111242825B (en) A zero-watermark method for ENC electronic nautical charts based on bathymetric features
CN101840473A (en) Vector map copyright protection method based on non-linear transformation
CN103377320B (en) A kind of vector geographic data selectivity certification semi-fragile watermarking method
CN103903217A (en) Vector map integrity authentication method based on vertex insertion
CN101847250B (en) A Blind Watermarking Method for Vector Map Data Based on DCT

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
CB03 Change of inventor or designer information
CB03 Change of inventor or designer information

Inventor after: Yan Haowen

Inventor after: Jiang Xiaoqin

Inventor after: Zhang Liming

Inventor after: Tian Kunrui

Inventor after: Wei Zheng

Inventor before: Jiang Xiaoqin

Inventor before: Yan Haowen

Inventor before: Zhang Liming

Inventor before: Tian Kunrui

Inventor before: Wei Zheng

GR01 Patent grant
GR01 Patent grant