Steganalysis by subtractive pixel adjacency matrix and dimensionality reduction

作     者：ZHANG Hao PING XiJian XU ManKun WANG Ran 

作者机构：Department of Signal and Information Processing Zhengzhou Information Science and Technology Institute 

出 版 物：《Science China(Information Sciences)》 (中国科学:信息科学(英文版))

年 卷 期：2014年第57卷第4期

页      面：286-292页

核心收录：

学科分类：08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 

基　　金：supported by National Natural Science Foundation of China(Grant No.60970142) 

主　　题：steganalysis Markov chain dimensionality reduction LSB matching YASS algorithm 

摘      要：Subtractive pixel adjacency matrix(SPAM)features,introduced by Pevn′y et *** a type of Markov chain features,are widely used for blind steganalysis in the spatial *** this paper,we present three improvements to SPAM as follows:1)new features based on parallel subtractive pixels are added to the SPAM features,which only refer to collinear subtractive pixels;2)features are extracted not only from the spatial image,but also from its grayscale-inverted image,making the feature matrices symmetrical and reducing their dimensionality by about half;and 3)a new kind of adjacency matrix is used,thereby reducing about 3/4 of the dimensionality of the *** results show that these methods for dimensionality reduction are very effective and that the proposed features outperform SPAM.