在分析了现有的可伸缩矢量图形(SVG)格式地图差异算法的研究现状和不足的基础上提出了一种改进的SVG格式地图差异匹配算法I-DiffS(improved difference of SVG maps).该改进算法定义了节点集元素,即节点集元素可能包含1个或多个元素节...在分析了现有的可伸缩矢量图形(SVG)格式地图差异算法的研究现状和不足的基础上提出了一种改进的SVG格式地图差异匹配算法I-DiffS(improved difference of SVG maps).该改进算法定义了节点集元素,即节点集元素可能包含1个或多个元素节点、属性节点和值节点构成的一个路径节点集合,定义了SVG格式解析结构树的标号规则,减少了结构树对应数组的元素个数,也减少了差异脚本中操作类型的数目,缩短了匹配过程.匹配结果为差异脚本,该脚本记录了前一个时间戳到后一个时间戳的更新操作.I-DiffS算法相比于现有的最新DiffS算法,时间复杂度更低.应用验证证明了该算法有效.展开更多
Nowadays,many steganographic tools have been developed,and secret messages can be imperceptibly transmitted through public networks.This paper concentrates on steganalysis against spatial least significant bit(LSB) ma...Nowadays,many steganographic tools have been developed,and secret messages can be imperceptibly transmitted through public networks.This paper concentrates on steganalysis against spatial least significant bit(LSB) matching,which is the prototype of many advanced information hiding methods.Many existing algorithms deal with steganalysis problems by using the dependencies between adjacent pixels.From another aspect,this paper calculates the differences among pixel pairs and proves that the histogram of difference values will be smoothed by stego noises.We calculate the difference histogram characteristic function(DHCF) and deduce that the moment of DHCFs(DHCFM) will be diminished after stego bits are hidden in the image.Accordingly,we compute the DHCFMs as the discriminative features.We calibrate the features by decreasing the influence of image content on them and train support vector machine classifiers based on the calibrated features.Experimental results demonstrate that the DHCFMs calculated with nonadjacent pixels are helpful to detect stego messages hidden by LSB matching.展开更多
文摘在分析了现有的可伸缩矢量图形(SVG)格式地图差异算法的研究现状和不足的基础上提出了一种改进的SVG格式地图差异匹配算法I-DiffS(improved difference of SVG maps).该改进算法定义了节点集元素,即节点集元素可能包含1个或多个元素节点、属性节点和值节点构成的一个路径节点集合,定义了SVG格式解析结构树的标号规则,减少了结构树对应数组的元素个数,也减少了差异脚本中操作类型的数目,缩短了匹配过程.匹配结果为差异脚本,该脚本记录了前一个时间戳到后一个时间戳的更新操作.I-DiffS算法相比于现有的最新DiffS算法,时间复杂度更低.应用验证证明了该算法有效.
基金supported by the NSFC(61173141,61362032,U1536206, 61232016,U1405254,61373133,61502242,61572258)BK20150925+4 种基金the Natural Science Foundation of Jiangxi Province, China(20151BAB207003)the Fund of Jiangsu Engineering Center of Network Monitoring(KJR1402)the Fund of MOE Internet Innovation Platform(KJRP1403)the CICAEET fundthe PAPD fund
文摘Nowadays,many steganographic tools have been developed,and secret messages can be imperceptibly transmitted through public networks.This paper concentrates on steganalysis against spatial least significant bit(LSB) matching,which is the prototype of many advanced information hiding methods.Many existing algorithms deal with steganalysis problems by using the dependencies between adjacent pixels.From another aspect,this paper calculates the differences among pixel pairs and proves that the histogram of difference values will be smoothed by stego noises.We calculate the difference histogram characteristic function(DHCF) and deduce that the moment of DHCFs(DHCFM) will be diminished after stego bits are hidden in the image.Accordingly,we compute the DHCFMs as the discriminative features.We calibrate the features by decreasing the influence of image content on them and train support vector machine classifiers based on the calibrated features.Experimental results demonstrate that the DHCFMs calculated with nonadjacent pixels are helpful to detect stego messages hidden by LSB matching.
