In　order　to　prevent　the　impact　of　difficult　to　recognize　genuine　and　fake　face　changing　images　on　people＇s　lives　and　social　stability,　it　is　urgent　to　study　efficient　deepfake　detection　algorithms　for　such　images.　However,　most　existing　deepfake　detection　algorithms　require　extracting　a　large　number　of　video　frames　from　dataset　videos　for　training.　To　address　this　issue,　this　paper　proposes　a　new　deepfake　detection　algorithm　based　on　a　multi-Attentional　model.　Firstly,　this　article　adds　and　fuses　channel　information　to　multiple　attention　maps　generated　by　multiple　spatial　attention　heads,　enabling　the　model　to　comprehensively　utilize　information　from　various　different　features　and　provide　richer　feature　representations.　Secondly,　in　the　texture　enhancement　section,　DenseBlock　is　used　to　expand　the　receptive　field　and　enhance　the　ability　to　extract　shallow　features,　enabling　the　model　to　capture　more　detailed　information.　This　article　uses　single　frame　real　faces　extracted　from　the　public　dataset　FaceForensics++,　a　few　sample　dataset　created　from　forged　faces　tampered　with　by　DeepFake,　and　a　dataset　created　from　forged　faces　tampered　with　by　our　own　image　editing　software　to　verify　the　performance　of　the　proposed　method.　The　experimental　results　show　that　on　both　datasets,　AUC　and　ACC　have　increased　by　1.8%,　2.1%,　and　1.7%,　3.9%,　respectively,　compared　to　the　original　model.　　©　2023　ACM.