典型文献
Binary Image Steganalysis Based on Symmetrical Local Residual Patterns
文献摘要：
Residual computation is an effective method for gray-scale image steganalysis.For binary im-ages,the residual computation calculated by the XOR op-eration is also employed in the local residual patterns(LRP)model for steganalysis.A binary image steganalyt-ic scheme based on symmetrical local residual patterns(SLRP)is proposed.The symmetrical relationships among residual patterns are introduced that make the features more compact while reducing the dimensionality of the features set.Multi-scale windows are utilized to construct three SLRP submodels which are further merged to con-struct the final features set instead of a single model.SLRPs with higher probability to be modified after em-bedding are emphasized and selected to construct the fea-ture sets for training the support vector machine classifi-er.The experimental results show that the proposed steganalytic scheme is effective for detecting binary im-age steganography.
文献关键词：
中图分类号：
作者姓名：
LUO Junwei;YU Mujian;YIN Xiaolin;LU Wei
作者机构：
School of Computer Science and Engineering,Guangdong Province Key Laboratory of Information Security Technology,Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing,Sun Yat-sen University,Guangzhou 510006,China
文献出处：
引用格式：
[1]LUO Junwei;YU Mujian;YIN Xiaolin;LU Wei-.Binary Image Steganalysis Based on Symmetrical Local Residual Patterns)[J].电子学报（英文）,2022(04):752-763
A类：
steganalyt,SLRP,submodels,SLRPs,steganalytic
B类：
Binary,Image,Steganalysis,Based,Symmetrical,Local,Residual,Patterns,computation,effective,method,gray,scale,image,steganalysis,For,binary,ages,residual,calculated,by,XOR,eration,also,employed,local,patterns,scheme,symmetrical,proposed,relationships,among,are,introduced,that,make,features,more,compact,while,reducing,dimensionality,Multi,windows,utilized,construct,three,which,further,merged,final,instead,single,higher,probability,modified,after,bedding,emphasized,selected,sets,training,support,vector,machine,classifi,experimental,results,show,detecting,steganography
AB值：
0.50691
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