The　Spatial　Rich　Model　(SRM)　generates　powerful　steganalysis　features,　but　has　high　computational　complexity　since　it　requires　calculating　tens　of　thousands　of　convolutions　with　image　noise　residuals.　Practical　applications　dealing　with　a　massive　amount　of　image　transferred　through　the　Internet　would　suffer　a　long　computing　time　if　using　CPU.　To　accelerate　the　steganalysis,　we　present　a　parallel　SRM　feature　extraction　algorithm　based　on　GPU　architecture.　We　exploit　parallelism　of　the　algorithm,　modify　the　original　SRM　extraction　algorithm　and　employ　some　strategies　to　avoid　the　disadvantage　of　its　sequentiality.　Some　OpenCL　optimization　technologies　are　also　used　to　accelerate　the　extraction　process,　such　as　convolution　unrolling,　combined　memory　access,　split-merge　strategy　for　co-occurrence　matrix　calculation.　The　experimental　results　show　that　the　speed　of　the　proposed　parallel　extraction　algorithm　for　different　size　images　is　25　similar　to　55　times　faster　than　the　original　single　thread　algorithm.　In　addition,　when　using　AMD　GPU　HD　6850,　our　algorithm　runs　2　similar　to　4.2　times　faster　than　using　a　Intel　Quad-core　CPU.　This　indicates　our　algorithm　makes　good　use　of　the　GPU　cores.