摘要
提出了一种全新的基于经验模态分解的数字高程模型(DEM)数据伪装技术。首先利用SHA-256单向Hash函数产生由种子控制的伪随机序列,扩充序列后再用经验模态分解生成用于伪装的DEM数据,伪装后的DEM数据具有较高的视觉欺骗性。同时针对DEM数据提出了广义直方图的概念,通过修改广义直方图在伪装的DEM数据中以便可逆地嵌入水印。本方法保证提取水印后可完全恢复伪装DEM数据以及使用种子可完全还原秘密DEM数据,算法安全性较高。
A new information disguising method based on empirical mode decomposition was proposed in this paper. The pseudorandom sequence controlled by seeds of the SHA-256 one-way hash function was generated, and Digital Elevation Model （DEM） data for disguising was achieved by decomposing the expanded pseudorandom sequence via EMD. The high vision fraudulence was obtained for disguised DEM data. Furthermore, the concept of the generalized histogram for DEM data was also proposed and the watermarking was reversibly embedded in the disguised DEM data by modifying its generalized histogram. The disguised DEM data could be completely reconstructed without any distortion from the marked data after the watermark had been extracted. The secret DEM data could be recovered via the seed. The proposed algorithm is of high security.
出处
《计算机应用》
CSCD
北大核心
2007年第6期1345-1348,共4页
journal of Computer Applications
基金
国家自然科学基金资助项目(60573027
60603014)
关键词
信息伪装
数字高程模型数据
经验模态分解
广义直方图
可逆数字水印
information disguising
Digital Elevation Model （DEM） data
Empirical Mode Decomposition （EMD）
generalized histogram
reversible digital watermarking
