This　work　proposes　a　spatial　steganalysis　scheme　based　on　local　textural　features　and　double　dimensionality　reduction.　First,　an　image　is　filtered　by　multiple　filters　to　obtain　a　number　of　residual　images.　Local　textural　patterns　are　obtained　by　comparing　the　pixel　values　with　the　neighbors＇　value　in　each　residual　image.　By　combining　all　local　textural　patterns,　a　high-dimensional　textural　feature　set　is　formed.　Then,　principal　component　analysis　is　used　to　perform　double　dimensionality　reduction　for　high-dimensional　textural　features.　In　the　first　dimensionality　reduction　stage,　the　correlation　from　the　same　filter　is　eliminated,　while　the　correlation　from　different　filters　can　be　also　eliminated　in　the　second　dimensionality　reduction　stage.　Finally,　a　textural　feature　set　with　low　dimensionality　is　proposed　and　can　be　effectively　used　in　steganalysis.　Experimental　results　show　that　proposed　textural　feature　set　can　efficiently　detect　adaptive　steganographic　schemes　in　spatial　domain.　Copyright　(c)　2014　John　Wiley　&　Sons,　Ltd.