Deep　learning,　as　an　important　branch　of　machine　learning,　has　been　widely　applied　in　computer　vision,　natural　language　processing,　speech　recognition,　and　more.　However,　recent　studies　have　revealed　that　deep　learning　systems　are　vulnerable　to　backdoor　attacks.　Backdoor　attackers　inject　a　hidden　backdoor　into　the　deep　learning　model,　such　that　the　predictions　of　the　infected　model　will　be　maliciously　changed　if　the　hidden　backdoor　is　activated　by　input　with　a　backdoor　trigger　while　behaving　normally　on　any　benign　sample.　This　kind　of　attack　can　potentially　result　in　severe　consequences　in　the　real　world.　Therefore,　research　on　defending　against　backdoor　attacks　has　emerged　rapidly.　In　this　article,　we　have　provided　a　comprehensive　survey　of　backdoor　attacks,　detections,　and　defenses　previously　demonstrated　on　deep　learning.　We　have　investigated　widely　used　model　architectures,　benchmark　datasets,　and　metrics　in　backdoor　research　and　have　classified　attacks,　detections　and　defenses　based　on　different　criteria.　Furthermore,　we　have　analyzed　some　limitations　in　existing　methods　and,　based　on　this,　pointed　out　several　promising　future　research　directions.　Through　this　survey,　beginners　can　gain　a　preliminary　understanding　of　backdoor　attacks　and　defenses.　Furthermore,　we　anticipate　that　this　work　will　provide　new　perspectives　and　inspire　extra　research　into　the　backdoor　attack　and　defense　methods　in　deep　learning.