The　current　hot　deep　steganography　can　hide　confidential　information　of　an　image　into　a　carrier　image　of　the　same　size.　It　also　has　the　characteristics　of　a　high　steganography　rate　and　is　often　used　for　private　communication　within　specific　organizations.　However,　the　existing　deep　steganography　detection　network　is　complex　and　time-consuming.　Thus,　rapid　steganography　detection　for　massive　image　information　is　urgently　needed.　In　this　study,　a　typical　distillation　algorithm　is　applied　to　the　existing　classical　deep　steganography　detection　network　(Ye-Net　and　Yedroudj-Net).　We　constructed　a　student　network,　taking　Ye-Net　and　Yedroudj-Net　as　teacher　networks　respectively.　Appropriate　parameters　T　and　α　are　also　selected　in　accordance　with　the　effect　of　knowledge　distillation.　With　these　parameters,　a　fast　training　model　of　deep　steganography　detection　is　established.　The　proposed　deep　steganography　detection　network　is　compared　with　Ye-Net　and　Yedroudj-Net　through　experiments.　Three　spatial　steganography　algorithms　(Wow,S-uniward　and　Hill)　and　different　steganography　rates　are　used.　The　experimental　results　show　that　the　proposed　network　can　shorten　the　training　and　average　detection　time　by　about　70%　and　24%　when　the　detection　accuracy　is　slightly　lower.　Overall,　compared　with　Ye-Net　and　Yedroudj-Net,　the　proposed　method　can　achieve　fast　detection　of　deep　steganography.　And　code　will　be　available　at:　https://github.com/hhfshiqi/KD-Ye-net　©　2021　ACM.