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Using Reversible Steganography Algorithm to Embed Metadata in Vector Maps

應用可逆式資料隱藏法將後設資料嵌入於向量地圖

摘要


向量地圖(Vector Maps)是很多地理資訊系統(GIS)發展與應用的基礎。向量地圖的後設資料(Metadata)除提供該地圖相關的詳細資料外,也是GIS發展中非常重要的參考資料。雖然後設資料的應用已有既定的基礎,但卻仍存在一些問題,尤其是其資料儲存的問題。本研究提出一個應用可逆式資料隱藏演算法,可將後設資料嵌入向量地圖。研究結果顯示,本研究可於向量地圖中嵌入2(n-2)位元的資料量,其中n代表此向量地圖的頂點數。與其它已知的研究成果相比,我們所提的方法具有最高的資料嵌入量。而本研究實驗結果顯示,一個向量地圖最少要有5458個頂點數,才能將完整的嵌入本研究所採用的ISO 19115後設資料標準中的所有項目。而最少要有1998頂點數,才能嵌入該標準所訂定的主要後設資料項目。實驗結果也顯示,本研究將後設資料擷取出來後的復原圖與原始圖間僅有肉眼所無法分辨出來的3.41E-11的RMSE誤差量。此結果可滿足任何GIS應用與發展上的精度需求。另我們的方法也具其強韌度,可抗拒外在平移、旋轉、等量縮放,以及其組合性的攻擊。

並列摘要


Vector maps are the fuel of many Geographic Information System (GIS) applications. Metadata, which is the data about vector maps, are introduced to provide the details of the vector maps. The usage of metadata is still facing some problems. Especially the storage of metadata is a problem that needs to be resolved. This paper presents an internal metadata storage mechanism for solving metadata storage problem by using a reversible steganographic algorithm to embed metadata in vector maps. Experimental results show that this method provides a solution for metadata embedding with high capacity but low distortion. The capacity of metadata embedding is 2(n-2) bits, where n is the amount of vertices of vector maps. To the best of our knowledge, our method provides the highest capaicty achieved in the literature of steganograhy for vector maps. In considering to the capacity required by the metadata elements of ISO 19115 metadata standard that we have adapted in this paper, a vector map should has at least 5458 vertices so that all mandatory and conditional metadata elements can be embedded in the vector map. Since the conditional elements should not be embedded alone, a vector map should has at least 1998 vertices so that the mandatory metadata elements can be embedded and integrated with the vector map. Experimental results also show that there is an insignificantly difference between the original and the recovery map, which is less than 3.41E-11 of the root mean square error (RMSE) and is imperceptible to the human visual system. Surely, the accuracy of recovery maps satisfies the requirements of all GIS applications development. Meanwhile, our method is robust against the affine transformation including translation, rotation, uniform scaling, and their combinations.

參考文獻


Bogomjakov, A.,Gotsman, C.,Isenburg, M.(2008).Distortion-Free Steganography for Polygonal Meshes.Computer Graphics Forum.27(2),637-642.
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Chao, M. W.,Lin, C. H.,Yu, C. W.,Lee, T. Y.(2009).A High Capacity 3D Steganography Algorithm.IEEE Transaction on Visualization and Computer Graphics.20(3),1-11.
Chen, K. W.,Wang, S. M.,Wang, C. M.(2007).A Reversible Data Hiding Algorithm for 2D Vector Map.(Communications of the CCISA).

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