Feature　fusion　method　has　improved　steganographic　detection　performance　based　on　classical　feature,　however　there　are　some　drawbacks　of　this:　without　analysing　the　correlation　of　the　basic　features,　it＇s　only　a　simple　combination　of　features　and　lacks　standard　for　features　selection;　serial　fusion　feature　always　has　high　dimension,　which　will　lead　great　time　cost　and　possibility　of　curse　of　dimensionality.　In　this　paper,　we　proposed　a　novel　framework　for　measuring　the　feature　selection　and　fusing　two　selected　feature　sets　in　steganographic　detection　field,　based　on　KCCA　theory.　KCCA　feature　fusion　method　can　outperform　single　feature　and　achieve　similar　performance　to　serial　feature　fusion　method　in　steganographic　detection　field,　while　only　costing　1/101/8　of　original　time.　So　it　has　better　practicability.　©　2011　IEEE.