Merging　multiple　features　can　achieve　higher　detection　rate　than　single　feature　in　universal　steganalysis,　however　there　are　drawbacks　in　most　merged　feature　steganalysis　methods:　it　is　only　a　simple　combination　of　features　without　analysing　the　relation　of　constisten　and　afoul　among　these　features,　besides　there　is　no　unified　standard　for　features　selection.　This　paper　introduces　a　quantity　to　capture　the　quantitative　measure　of　agreement　between　different　features,　which　we　call　alignment,　for　feature　kernel　selection　in　steganographic　detection　of　JPEG　images　based　on　Multiple　Kernel　SVM(MK-SVM).　We　apply　orthogonal　feature　sets　with　low　alignment　value　as　the　＇basis　kernels　on　features＇　in　multiple　kernel　learning　model,　the　linear　combination　of　kernels　is　optimized　using　SimpleMKL　algorithm.　We　compare　the　performance　of　merged　feature　method　with　multiple　kernel　method　to　detect　six　popular　steganographic　algorithms,　result　indicates　that　multiple　kernel　method　outperforms　merged　feature　method　for　0.2%-1.7%,　and　most　importantly　with　theory　evidence　for　features　selection.　©　2012　IEEE.