Abstract: In this paper, the author put forward a Least Significant Bit (LSB) matching steganalytic algorithm. Through calculating the second-order differential of the image pixels on horizontal and vertical directions, the differential matrix was obtained and it was used as sensitive feature extracting source and the second-order Markov transformation matrix of the differential matrix was extracted as the features, according to the LSB matching steganography with high security. According to the experimental result shows that the algorithm proposed in the paper speedups the detection process in a large extent and enhances the performance and practicability of the steganalytic algorithm while maintaining high detection accuracy, compared with the algorithms based on first-order differential Markov transition probability matrix.

Key words: Least Significant Bit (LSB) matching, steganalysis, second-order differential, Markov feature

摘要： 针对安全性较高的最不重要位(LSB)匹配隐写算法，通过计算待检测图像像素水平和垂直方向的二阶差分，得到二阶差分矩阵并将其作为敏感特征提取源，提取差分矩阵的二阶Markov转移概率矩阵作为特征，提出了一种隐写检测算法。实验结果表明：与基于一阶差分Markov转移概率矩阵的算法相比，该算法在保证检测较高正确率的情况下，在很大程度上提高了算法的检测速度，增强了算法的性能和实用性。

关键词: 最不重要位匹配, 隐写检测, 二阶差分, Markov特征