Face-spoofing　detection　based　on　deep　convolutional　neural　networks　has　achieved　good　performance　in　recent　years.　However,　deep　neural　networks　are　vulnerable　to　adversarial　examples,　which　will　reduce　the　safety　of　the　face　based　application　systems.　Therefore,　it　is　necessary　to　analyze　the　mechanism　of　generating　the　adversarial　examples,　so　that　the　face-spoofing　detection　algorithms　will　be　more　robust.　Compared　with　the　general　classification　problems,　face-spoofing　detection　has　the　smaller　inter-class　distance,　and　the　perturbation　is　difficulty　to　assign.　Motivated　by　the　above,　this　study　proposes　an　approach　to　generate　the　adversarial　examples　for　face-spoofing　detection　by　combining　the　minimum　perturbation　dimensions　and　visual　concentration.　In　the　proposed　approach,　perturbation　is　concentrated　on　a　few　pixels　in　a　single　component,　and　the　intervals　between　pixels　are　constrained-according　to　the　visual　concentration.　With　such　constraints,　the　generated　adversarial　examples　can　be　perceived　by　human　with　low　probability.　The　adversarial　examples　generated　from　the　proposed　approach　can　defraud　the　deep　neural　networks　based　classifier　with　only　1.36%　changed　pixels　on　average.　Furthermore,　human　vision　perception　rate　of　the　proposed　approach　decreases　about　20%　compared　with　DeepFool.　©　Copyright　2019,　Institute　of　Software,　the　Chinese　Academy　of　Sciences.　All　rights　reserved.