Journal of Geo-information Science >
Study on Secret Classification Marking Method for Vector Geographic Data
Received date: 2016-06-01
Request revised date: 2016-08-16
Online published: 2017-03-20
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Secret classification marking is hidden information used to mark the security level of information products and is the basis and precondition for classification management, control and application of information. Since vector geographic data is the main organization format of classified geographic information products, it is of great significance to study secret classification marking method for vector geographic data. When suffered from geometry attack, the spatial relationship among elements of vector geographic data is invariable and secret classification marking method requires good imperceptibility and high robustness. In order to realize classification management and protection of vector geographic data, this paper presents a secret classification marking method for vector geographic data based on distance ratio modulating. In essence, using the digital watermark technology, the method embeds secret classification marking as watermarking information into vector geographic data, which has important application value for security protection of vector geographic data. The main ideas related to the study are as follows: Firstly, the method is based on distance ratio modulating and it can deal with geometry attack very well. Secondly, the method effectively improves the robustness against editing attack by repeatedly embedding secret classification marking within each line and area. Lastly, through embedding secret classification marking into feature points which are extracted by using Douglas-Pecker algorithm, the method can also effectively resist data compression attack. The experimental results show that the proposed method has good imperceptibility. In addition, when vector geographic data is suffered from the attacks, such as geometric transformation, data editing and data compression, the extraction ratio of its secret classification marking can reach almost more than 80%, indicating the robustness of this method.
LI Mengyuan , LI Anbo , ZHOU Wei , JIANG Wei . Study on Secret Classification Marking Method for Vector Geographic Data[J]. Journal of Geo-information Science, 2017 , 19(3) : 298 -306 . DOI: 10.3724/SP.J.1047.2017.00298
Fig. 1 Basic idea of the algorithm图1 算法基本思路 |
Fig. 2 Generation of the secret classification marking图2 密级标识的生成 |
Fig. 3 Reading and writing of vector geographic data based on OGR图3 基于OGR的矢量地理数据的读写 |
Fig. 4 The experimental data图4 实验数据 |
Tab. 1 The relevant indicators of precision analysis表1 精度分析的相关指标 |
调制位 | RMSE.X | RMSE.Y | 最大X误差 | 最大Y误差 |
---|---|---|---|---|
6 7 8 | 0.67E-4 0.63E-5 0.66E-6 | 0.12E-3 0.88E-5 0.99E-6 | 0.00516 0.00034 0.00004 | 0.01047 0.00070 0.00008 |
Fig. 5 The vector map of embedding secret classification marking after geometry attack图5 几何攻击后嵌入密级标识的矢量图 |
Fig. 6 Five kinds of cropping patterns图6 5种裁剪模式 |
Tab. 2 The robustness of secret classification marking after geometry attack表2 几何攻击的鲁棒性 |
攻击方式 | 攻击模式 | 变换因子 | 提取率/% |
---|---|---|---|
缩放 | A1 | 0.2 | 98 |
A2 | 0.5 | 99 | |
A3 | 2 | 99 | |
平移 | B1 | 1E+2 | 99 |
B2 | 1E+5 | 99 | |
B3 | 1E+7 | 96 | |
旋转 | C1 | 60° | 99 |
C2 | 90° | 99 | |
C3 | 150° | 99 |
Tab. 3 The robustness of secret classification marking after cropping attack表3 裁剪攻击的鲁棒性 |
裁剪模式 | 裁剪后要素数/个 | 裁剪后点数/个 | 提取率/% |
---|---|---|---|
D1 D2 D3 D4 D5 | 89 74 193 131 198 | 1636 1465 3216 2231 3487 | 84 90 81 83 88 |
Tab. 4 The robustness of secret classification marking after adding or removing points表4 增、删点攻击的鲁棒性 |
编辑模式 | 原点数/个 | 编辑点数(占总点数百分比) | 提取率/% |
---|---|---|---|
数据增点 | 10 609 | 531(5%) | 88 |
10 609 | 1061(10%) | 81 | |
10 609 | 3183(30%) | 63 | |
数据删点 | 10 609 | 531(5%) | 93 |
10 609 | 1061(10%) | 88 | |
10 609 | 3183(30%) | 69 |
Tab. 5 The robustness of secret classification marking after data compression attack表5 压缩攻击的鲁棒性 |
原点数/个 | 压缩阈值 | 压缩后点数/个 | 压缩率/% | 提取率/% |
---|---|---|---|---|
10 609 10 609 10 609 10 609 10 609 | 1.0 2.5 3.0 3.2 3.5 | 5942 4048 3753 3658 3546 | 56 38 35 34 33 | 96 99 100 89 75 |
Tab. 6 The robustness of secret classification marking after projection transformation attack表6 投影变换攻击的鲁棒性 |
攻击方式 | 提取率/% | 还原后提取率/% |
---|---|---|
Lambert 投影变换 Mercator 投影变换 | 0 0 | 99 99 |
Tab. 7 The robustness of secret classification marking after combination attack表7 组合攻击的鲁棒性 |
组合类型 | 压缩阈值 | 压缩后点数/个 | 变换因子 | 裁剪后点数/个 | 提取率 /% |
---|---|---|---|---|---|
压缩+缩放 压缩+裁剪 平移+裁剪 压缩+平移+裁剪 | 1.0 1.0 - 1.0 | 5942 5942 - 5942 | 2 - 1E+2 1E+2 | - 868 1465 868 | 提取失败 87 90 87 |
Fig. 7 Detecting threshold by exhaustive method图7 穷举法探测阈值 |
Fig. 8 The relationship between data size and consuming time图8 数据量与耗时关系 |
The authors have declared that no competing interests exist.
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