The　guarantee　of　social　stability　comes　from　many　aspects　of　life,　and　image　information　security　as　one　of　them　is　being　subjected　to　various　malicious　attacks.　As　a　means　of　information　attack,　image　splicing　forgery　refers　to　copying　some　areas　of　an　image　to　another　image　to　hide　the　traces　of　the　original　information　and　leads　to　grave　consequences.　Image　splicing　forgery　is　extremely　complex　since　the　attributes　of　the　two　images　subjected　to　the　pasting　and　copying　operations　are　greatly　different.　In　order　to　solve　the　issue　mentioned　above,　we　propose　a　method　by　applying　a　neural　learning　network　controlled　by　multiple　scales　(MCNL-Net)　based　on　U-Net　to　identify　whether　an　image　has　been　tampered　and　to　locate　the　tampered　regions.　Firstly,　the　learning　capacity　of　MCNL-Net　is　enhanced　by　the　combination　of　a　residual　propagation　module　and　a　residual　feedback　module.　An　ingenious　strategy　is　designed　to　control　the　size　of　local　receptive　field　in　each　building　block　of　MCNL-Net.　The　strategy　makes　MCNL-Net　able　to　achieve　properties　and　superiorities　of　multi-scale　structure　and　learn　specified　features.　For　further　improving　the　detection　performance　of　MCNL-Net,　a　block　attention　mechanism　is　proposed　to　control　the　advanced　degree　of　the　input　information　in　each　building　block.　In　addition,　a　MaxBlurPool　method　is　applied　into　image　splicing　forgery　detection　for　the　first　time,　preserving　the　shift-equivariance　of　a　convolutional　neural　network.　Through　experiments,　we　demonstrate　that　MCNL-Net　can　achieve　more　promising　results　and　offer　stronger　robustness　than　the　state-of-the-art　splicing　forgery　detection　methods.　©　2020　ACM.