Since　facial　forgery　techniques　have　made　remarkable　progress,　the　area　of　forgery　detection　attracts　a　significant　amount　of　attention　due　to　security　concerns.　Existing　methods　attempt　to　utilize　convolutional　neural　networks　(CNNs)　to　mine　discriminative　clues　for　forgery　detection.　However,　most　of　these　coarse-grained　and　vanilla　methods　struggle　to　extract　subtle　and　multiscale　clues　in　forgery　detection.　To　address　such　problems,　we　propose　a　well-designed　deep　learning　framework,　named　SCA-Net,　to　exploit　subtle,　multiscale　and　multiview　clues.　Specifically,　our　framework　consists　of　a　skipped　channel　attention　module　(SCM),　a　constrained　difference　module　(CDM)　and　an　adaptive　attention　module　(AAM).　First,　the　skipped　channel　attention　module　is　used　as　the　backbone　to　extract　sufficient　different　information,　including　low-level　and　high-level　features.　Then,　the　constrained　difference　module　captures　manipulation　clues　from　the　input　image　based　on　constrained　characteristics.　Finally,　the　adaptive　attention　module　captures　multiscale　features　represented　by　facial　forgery.　Moreover,　we　introduce　a　combined　loss　to　address　the　learning　difficulty　of　our　framework.　The　experimental　results　demonstrate　that　the　proposed　model　has　great　detection　performance　compared　with　other　face　forgery　detection　methods　in　most　cases.　©　2023,　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.