With　the　rapid　development　and　popularity　of　the　network,　the　openness,　anonymity,　and　interactivity　of　networks　have　led　to　the　spread　and　proliferation　of　pornographic　images　on　the　Internet,　which　have　done　great　harm　to　adolescents＇　physical　and　mental　health.　With　the　establishment　of　image　compression　standards,　pornographic　images　are　mainly　stored　with　compressed　formats.　Therefore,　how　to　efficiently　filter　pornographic　images　is　one　of　the　challenging　issues　for　information　security.　A　pornographic　image　recognition　and　filtering　method　in　the　compressed　domain　is　proposed　by　using　incremental　learning,　which　includes　the　following　steps:　(1)　low-resolution　(LR)　images　are　first　reconstructed　from　the　compressed　stream　of　pornographic　images,　(2)　visual　words　are　created　from　the　LR　image　to　represent　the　pornographic　image,　and　(3)　incremental　learning　is　adopted　to　continuously　adjust　the　classification　rules　to　recognize　the　new　pornographic　image　samples　after　the　covering　algorithm　is　utilized　to　train　and　recognize　the　visual　words　in　order　to　build　the　initial　classification　model　of　pornographic　images.　The　experimental　results　show　that　the　proposed　pornographic　image　recognition　method　using　incremental　learning　has　a　higher　recognition　rate　as　well　as　costing　less　recognition　time　in　the　compressed　domain.　(C)　2015　SPIE　and　IS&T