Image　splicing　forgery,　that　is,　copying　some　parts　of　an　image　into　another　image,　is　one　of　the　frequently　used　tampering　methods　in　image　forgery.　As　a　research　hotspot　in　recent　years,　deep　learning　has　been　used　in　image　forgery　detection.　However,　current　deep　learning　methods　have　two　drawbacks:　first,　they　are　too　simple　in　feature　fusion;　second,　they　rely　only　on　a　single　cross-entropy　loss　as　the　loss　function,　leading　to　models　prone　to　overfitting.　To　address　these　issues,　a　image　splicing　forgery　localization　method　based　on　multi-scale　supervised　U-shaped　network,　named　MSU-Net,　is　proposed　in　this　paper.　First,　a　triple-stream　feature　extraction　module　is　designed,　which　combines　the　noise　view　and　edge　information　of　the　input　image　to　extract　semantic-related　and　semantic-agnostic　features.　Second,　a　feature　hierarchical　fusion　mechanism　is　proposed　that　introduces　a　channel　attention　mechanism　layer　by　layer　to　perceive　multi-level　manipulation　trajectories,　avoiding　the　loss　of　information　in　semantic-related　and　semantic-agnostic　shallow　features　during　the　convolution　process.　Finally,　a　strategy　for　multi-scale　supervision　is　developed,　a　boundary　artifact　localization　module　is　designed　to　compute　the　edge　loss,　and　a　contrastive　learning　module　is　introduced　to　compute　the　contrastive　loss.　Through　extensive　experiments　on　several　public　datasets,　MSU-Net　demonstrates　high　accuracy　in　localizing　tampered　regions　and　outperforms　state-of-the-art　methods.　Additional　attack　experiments　show　that　MSU-Net　exhibits　good　robustness　against　Gaussian　blur,　Gaussian　noise,　and　JPEG　compression　attacks.　Besides,　MSU-Net　is　superior　in　terms　of　model　complexity　and　localization　speed.