With　the　technologies　of　blind　steganalysis　becoming　increasingly　popular,　a　growing　number　of　researchers　concern　in　this　domain.　Supervised　learning　for　classification　is　widely　used,　but　this　method　is　often　time　consuming　and　effort　costing　to　obtain　the　labeled　data.　In　this　paper,　an　improved　semi-supervised　learning　method:　path-based　transductive　support　vector　machines　(TSVM)　algorithm　with　Mahalanobis　distance　is　proposed　for　blind　steganalysis　classification,　by　using　modified　connectivity　kernel　matrix　to　improve　the　classification　accuracy.　Experimental　results　show　that　our　proposed　algorithm　achieves　the　highest　accuracy　among　all　examined　semi-supervised　TSVM　methods,　especially　for　a　small　labeled　data　set.　©　2009　Springer-Verlag.