The　steganalysis　method　based　on　spacing　statistics　of　short　duplicate　code　is　an　efficient　universal　detection　algorithm　for　LSB　matching　steganography.　But　the　steganalysis　method　must　select　the　appropriate　dimension　of　short　duplicate　code　to　meet　the　different　applications.　This　one-dimensionstatistical　analysis　method　could　not　take　into　account　the　links　between　multivariate　statistical　features,　thus　may　affect　the　detection　capability.　In　this　paper,　a　detection　capability　law　of　a　single　short　duplicate　code　statistical　feature　is　proved,　and　a　method　to　reasonable　choice　of　the　short　duplicate　code　dimension　is　presented　to　reduce　the　detection　number.　By　analyzing　the　correlation　between　the　statistical　features　of　short　duplicate　code　spacing　statistics,　a　selected　feature　subset　is　described　as　a　vector　of　local　features.　Then,　a　universal　steganalysis　method　based　on　local　features　extracted　from　LSB　sequences　is　proposed.　The　proposed　steganalysis　method　uses　the　Gaussian　Mixture　Model　(GMM)　to　describe　the　multi-dimensional　local　features,　and　designs　classifier　by　integrating　GMM　generative　model　and　SVM　discriminative　method　based　on　global　sequence　vocabulary.　The　experimental　results　show　that　under　the　premise　of　effective　control　of　the　false　alarm　rate,　the　proposed　method　achieves　the　best　overall　detection　performance　to　LSB　matching　steganography　and　to　LSB　replacement　steganography.