Backdoor　injection　attack　is　an　emerging　threat　to　the　security　of　neural　networks,　however,　there　still　exist　limited　effective　defense　methods　against　the　attack.　In　this　paper,　we　propose　BAERASER,　a　novel　method　that　can　erase　the　backdoor　injected　into　the　victim　model　through　machine　unlearning.　Specifically,　BAERASER　mainly　implements　backdoor　defense　in　two　key　steps.　First,　trigger　pattern　recovery　is　conducted　to　extract　the　trigger　patterns　infected　by　the　victim　model.　Here,　the　trigger　pattern　recovery　problem　is　equivalent　to　the　one　of　extracting　an　unknown　noise　distribution　from　the　victim　model,　which　can　be　easily　resolved　by　the　entropy　maximization　based　generative　model.　Subsequently,　BAERASER　leverages　these　recovered　trigger　patterns　to　reverse　the　backdoor　injection　procedure　and　induce　the　victim　model　to　erase　the　polluted　memories　through　a　newly　designed　gradient　ascent　based　machine　unlearning　method.　Compared　with　the　previous　machine　unlearning　solutions,　the　proposed　approach　gets　rid　of　the　reliance　on　the　full　access　to　training　data　for　retraining　and　shows　higher　effectiveness　on　backdoor　erasing　than　existing　fine-tuning　or　pruning　methods.　Moreover,　experiments　show　that　BAERASER　can　averagely　lower　the　attack　success　rates　of　three　kinds　of　state-of-the-art　backdoor　attacks　by　99%　on　four　benchmark　datasets.