Image　tampering　techniques　can　lead　to　distorted　or　misleading　information,　which　in　turn　poses　a　threat　in　many　areas,　including　social,　legal　and　commercial.　Numerous　image　tampering　detection　algorithms　lose　important　low-level　detail　information　when　extracting　deep　features,　reducing　the　accuracy　and　robustness　of　detection.　In　order　to　solve　the　problems　of　current　methods,　this　paper　proposes　a　new　network　called　IFE-Net　to　detect　three　types　of　tampered　images,　namely　copy-move,　heterologous　splicing　and　removal.　Firstly,　this　paper　constructs　the　noise　stream　using　the　attention　mechanism　CBAM　to　extract　and　optimize　the　noise　features.　The　high-level　features　are　extracted　by　the　backbone　network　of　RGB　stream,　and　the　FEASPP　module　is　built　for　capturing　and　enhancing　the　features　at　different　scales.　In　addition,　in　this　paper,　the　initial　features　of　RGB　stream　are　additionally　supervised　so　as　to　limit　the　detection　area　and　reduce　the　false　alarm.　Finally,　the　final　prediction　results　are　obtained　by　fusing　the　noise　features　with　the　RGB　features　through　the　Dual　Attention　Mechanism　(DAM)　module.　Extensive　experimental　results　on　multiple　standard　datasets　show　that　IFE-Net　can　accurately　locate　the　tampering　region　and　effectively　reduce　false　alarms,　demonstrating　superior　performance.　©　2024　Elsevier　B.V.