The　use　of　image-splicing　technologies　had　detrimental　effects　on　the　security　of　multimedia　information.　Hence,　it　is　necessary　to　develop　effective　methods　for　detecting　and　locating　such　tampering.　Previous　studies　have　mainly　focused　on　the　supervisory　role　of　the　mask　on　the　model.　The　mask　edges　contain　rich　complementary　signals,　which　help　to　fully　understand　the　image　and　are　usually　ignored.　In　this　paper,　we　propose　a　new　network　named　EAU-Net　to　detect　and　locat　the　splicing　regions　in　the　image.　The　proposed　network　consists　of　two　parts:　Edge-guided　SegFormer　and　Sparse-connected　U-Net　(SCU).　Firstly,　the　feature　extraction　module　captures　local　detailed　cues　and　global　environment　information,　which　are　used　to　deduce　the　initial　location　of　the　affected　regions　by　SegFormer.　Secondly,　a　Sobel-based　edge-guided　module　(EGM)　is　proposed　to　guide　the　network　to　explore　the　complementary　relationship　between　splicing　regions　and　their　boundaries.　Thirdly,　in　order　to　achieve　more　precise　positioning　results,　SCU　is　used　as　postprocessing　for　removing　false　alarm　pixels　outside　the　focusing　regions.　In　addition,　we　propose　an　adaptive　loss　weight　adjustment　algorithm　to　supervise　the　network　training,　through　which　the　weights　of　the　mask　and　the　mask　edge　can　be　automatically　adjusted.　Extensive　experimental　results　show　that　the　proposed　method　outperforms　the　state-of-the-art　splicing　detection　and　localization　methods　in　terms　of　detection　accuracy　and　robustness.