基于局部概率密度不变特征的强鲁棒数字图像水印关键技术研究

项目名称： 基于局部概率密度不变特征的强鲁棒数字图像水印关键技术研究

项目编号： No.61272416

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 自动化技术、计算机技术

项目作者： 王向阳

作者单位： 辽宁师范大学

项目金额： 75万元

中文摘要： 作为多媒体作品版权保护的有效手段，数字水印技术已成为国际学术界的研究热点。但是，现有绝大多数图像水印算法仅仅能够对抗常规的信号处理和部分简单的全局仿射变换，而无法有效抵抗一般性去同步攻击，即抗去同步攻击的图像水印算法研究仍然是一项富有挑战性的工作。本项目将采用概率密度建模技术，研究高度鲁棒的数字图像水印理论与方法，确定影响图像水印鲁棒性的关键因素，建立基于局部概率密度不变特征的可有效抵抗去同步攻击的强鲁棒数字图像水印整体系统。包括构造多尺度概率密度图像特征点检测器；建立基于概率密度Hessian矩阵与Tsallis熵的完全仿射不变局部特征区域；推导快速指数不变矩计算方法；设计基于视觉掩蔽的自适应图像水印方案及相应的并行化算法；研究智能图像水印代理技术和基于此技术的图像水印软件系统。本项目最终将建立起基于局部概率密度不变特征的数字图像水印模型，并研制出基于该模型的图像水印Agent软件包。

中文关键词： 图像水印；去同步攻击；局部概率密度不变特征；Tsallis熵；快速指数不变矩

英文摘要： .The rapid development of new information technologies has improved the ease of access to digital information. It also leads to the problem of illegal copying and redistribution of digital media. The concept of digital watermarking came up while trying to solve the problems related to the management of intellectual property of media. A digital image watermarking scheme must be robust against a variety of possible attacks. Attacks which attempt to destroy or invalidate watermarks can be classified into two types, noise-like common signal processing and desynchronization attacks. Desynchronization attacks are more difficult to tackle than other types of attacks. It is a challenging work to design a robust image watermarking scheme against desynchronization attacks. In recent years, several watermarking approaches that counterattack desynchronization attacks have been developed. These approaches can be roughly divided into Exhaustive search, Spread spectrum modulation, Invariant domain, Synchronization correction, and Feature-based algorithm. Through in-depth research and analysis, we observed that the former four image watermarking approaches are usually vulnerable to some desynchronization attacks, and the feature-based watermarking methods exhibit more promising than others in terms of robustness. However, there

英文关键词： Image watermarking；Desynchronization attack；Local invariant probability density feature；Tsallis entropy；Fast exponent invariant moments