基于Markov链安全性的量化隐写算法

A novel quantization-embedded steganographic algorithm based on Markov chain security

如何在大容量嵌入的同时保证隐秘信息的统计安全性是当前隐写算法设计的难点和热点。本文证明了载体图像Markov链(MC)二阶统计特性的保持等价于其一阶统计特性保持的结论,并据此以MC安全标准为优化目标,提出二阶安全的图像预测误差量化隐写算法。实验表明,在较大容量的数据嵌入过程中,该算法能很好保持二阶统计特性,取得隐写安全性的提高。

It is a difficulty and hotspot in steganographic algorithm designs to ensure the statistical security of secret information while large numbers of data are embedded.It′s proven that the maintenance of the second-order Markov Chain distribution of cover image is equivalent to that of its first-order distribution.Based on the conclusion,a novel second-order secure quantization-embedded steganographic algorithm in prediction-error domain is proposed,which employs Markov Chain security benchmarking as the optimization goal.Experimental results show that the proposed algorithm can maintain the second-order distribution of cover image while larger secret information is embedded,and improve the steganographic security.