To　cope　with　the　threat　of　image　content　tampering　in　real　scenes,　this　paper　develops　a　multi-view　spatial-channel　attention　network　(MSCA-Net),　which　can　use　multi-view　features　and　multi-scale　features　to　detect　whether　an　image　has　been　tampered　with　and　predict　tampered　regions.　By　introducing　the　frequency　domain　view　of　the　image,　the　model　can　use　the　noise　distribution　around　the　tampered　region　to　learn　semantically　independent　features　and　detect　subtle　tampering　traces　that　are　difficult　to　detect　in　the　RGB　domain.　Secondly,　a　new　Efficient　Spatial-Channel　Attention　Module　(ESCM)　is　proposed　to　capture　the　correlation　between　different　channels　and　between　global　pixels.　MSCA-Net　improves　the　localization　performance　of　tampered　regions　on　real-scene　images　by　generating　segmentation　masks　step　by　step　at　multiple　scales　through　a　progressive　guidance　mechanism.　MSCA-Net　runs　very　fast　and　is　capable　of　processing　1080P　resolution　images　at　40FPS+.　Extensive　experimental　results　demonstrate　the　promising　performance　of　MSCA-Net　on　both　image-level　and　pixel-level　tampering　detection　tasks.　©　2024　SPIE.　All　rights　reserved.