Traditional　blind　detection　techniques　for　BMP　stego　images　mainly　use　a　single　feature　set　and　a　single　classifier.　However,　a　single　feature　set　is　difficult　to　completely　reflect　the　differences　caused　by　embedding,　and　a　single　classifier　is　also　sensitive　to　samples.　Therefore,　we　propose　a　blind　detection　algorithm　based　on　feature　fusion　and　ensemble　classification　to　improve　the　accuracy　of　blind　detection　for　BMP　stego　images.　We　firstly　extract　the　features　based　on　higher-order　probability　density　function　(PDF)　moments　of　the　decomposition　subband　coefficients　and　statistical　moments　of　characteristic　function　(CF)　of　subband　histograms,　and　then　use　serial　feature　fusion　to　construct　a　new　feature　set,　adopt　Bagging　and　RSM　to　train　base　classifiers　and　finally　utilize　the　trained　classifiers　to　detect　images.　The　experiment　results　show　that　the　proposed　method　can　improve　the　accuracy　of　the　common　BMP　steganographic　methods,　such　as　LSB　repalcement,　LSB　matching,　SS,　and　QIM.