With　the　rapid　development　of　image　processing　technology,　it　has　become　increasingly　easy　to　manipulate　images,　which　poses　a　threat　to　the　stability　and　security　of　people＇s　lives.　Recent　methods　have　proposed　the　fusion　of　RGB　and　noise　features　to　uncover　tampering　traces.　However,　these　approaches　overlook　the　characteristics　of　features　at　different　levels,　leading　to　insufficient　feature　fusion.　To　address　this　problem,　this　paper　proposes　a　double-stream　multilevel　feature　fusion　network　(DMFF-Net).　Unlike　the　traditional　feature　fusion　approach,　DMFF-Net　adopts　a　graded　feature　fusion　strategy.　It　classifies　features　into　primary,　intermediate,　and　advanced　levels　and　introduces　the　Primary　Feature　Fusion　Module　(PFFM)　and　the　Advanced　Feature　Fusion　Module　(AFFM)　to　achieve　superior　fusion　results.　Additionally,　a　multisupervision　strategy　is　employed　to　decode　the　fused　features　into　level-specific　masks,　including　boundary,　regular,　and　refined　masks.　The　DMFF-Net　is　validated　on　publicly　available　datasets,　including　CASIA,　Columbia,　COVERAGE,　and　NIST16,　as　well　as　a　real-life　manipulated　image　dataset,　IMD20,　and　achieves　AUCs　of　84.7%,　99.6%,　86.6%,　87.4%　and　82.8%,　respectively.　Extensive　experiments　show　that　our　DMFF-Net　outperforms　state-of-the-art　methods　in　terms　of　image　manipulation　localization　accuracy　and　exhibits　improved　robustness.