Image　manipulation　has　sparked　widespread　concern　due　to　its　potential　security　threats　on　the　Internet.　The　boundary　between　the　authentic　and　manipulated　region　exhibits　artifacts　in　image　manipulation　localization　(IML).　These　artifacts　are　more　pronounced　in　heterogeneous　image　splicing　and　homogeneous　image　copy-move　manipulation,　while　they　are　more　subtle　in　removal　and　inpainting　manipulated　images.　However,　existing　methods　for　image　manipulation　detection　tend　to　capture　boundary　artifacts　via　explicit　edge　features　and　have　limitations　in　effectively　addressing　subtle　artifacts.　Besides,　feature　redundancy　caused　by　the　powerful　feature　extraction　capability　of　large　models　may　prevent　accurate　identification　of　manipulated　artifacts,　exhibiting　a　high　false-positive　rate.　To　solve　these　problems,　we　propose　a　novel　edge-aware　network　(EAN)　to　capture　boundary　artifacts　effectively.　This　network　treats　the　image　manipulation　localization　problem　as　a　segmentation　problem　inside　and　outside　the　boundary.　In　EAN,　we　develop　an　edge-aware　mechanism　to　refine　implicit　and　explicit　edge　features　by　the　interaction　of　adjacent　features.　This　approach　directs　the　encoder　to　prioritize　the　desired　edge　information.　Also,　we　design　a　multi-feature　fusion　strategy　combined　with　an　improved　attention　mechanism　to　enhance　key　feature　representation　significantly　for　mitigating　the　effects　of　feature　redundancy.　We　perform　thorough　experiments　on　diverse　datasets,　and　the　outcomes　confirm　the　efficacy　of　the　suggested　approach,　surpassing　leading　manipulation　localization　techniques　in　the　majority　of　scenarios.