Face　anti-spoofing　based　on　deep　learning　achieved　good　accuracy　recently.　However,　deep　learning　model　has　no　explicit　mathematical　presentation.　Therefore,　it　is　not　clear　about　how　the　model　works　effectively.　In　this　paper,　we　estimate　the　regions　in　face　image,　which　are　sensitive　in　deep　learning　based　anti-spoofing　algorithms.　We　first　generate　the　adversarial　examples　from　two　different　gradient-based　methods.　Then　we　analyze　the　distribution　of　the　gradient　and　perturbations　on　the　adversarial　examples.　And　next　we　obtain　the　sensitive　regions　and　evaluate　the　contribution　of　these　regions　to　classification　performance.　By　analyzing　the　sensitive　regions,　it　could　be　observed　that　the　CNN　based　anti-spoofing　algorithms　are　sensitive　to　rich　detailed　regions　and　illumination.　These　observations　are　helpful　to　design　an　effective　face　anti-spoofing　algorithm.