Curvelet Domain Watermark Detection Using Alpha-Stable Models

摘要：

This paper address issues that arise in copyright protection systems of digital images, which employ blind watermark verification structures in the curvelet domain. First, we observe that statistical distribution with heavy algebraic tails, such as the alpha-stable family, are in many cases more accurate modeling tools for the curvelet coefficients than families with exponential tails such as generalized Gaussian. Motivated by our modeling results, we then design a new processor for blind watermark detection using the Cauchy member of the alpha-stable family. We analyze the performance of the new detector in terms of the associated probabilities of detection and false alarm and we compare it to the performance of the generalized Gaussian detector and the traditional correlation-based detector by performance experiments. The experiments prove that Cauchy detector is superior to the others.

关键词： watermarking alpha-stable model curvelet locally most powerful

作者: Chengzhi Deng Huasheng Zhu Shengqian Wang

作者单位: Department of Computation Science & Technology Nanchang Institute of Technology Nanchang,China

会议类型: 国际会议

会议名称: The Fifth International Conference on Information Assurance and Security(第五届信息保障与安全国际会议)

会议地点: 西安

会议语种:英文

页码: 313-316

在线出版日期: 2009-08-18（万方平台首次上网日期，不代表论文的发表时间）