Backdoor　attacks　and　adversarial　attacks　are　two　major　security　threats　to　deep　neural　networks　(DNNs),　with　the　former　one　is　a　training-time　data　poisoning　attack　that　aims　to　implant　backdoor　triggers　into　models　by　injecting　trigger　patterns　into　training　samples,　and　the　latter　one　is　a　testing-time　attack　trying　to　generate　adversarial　examples　(AEs)　from　benign　images　to　mislead　a　well-trained　model.　While　previous　works　generally　treat　these　two　attacks　separately,　the　inherent　connection　between　these　two　attacks　is　rarely　explored.　In　this　paper,　we　focus　on　bridging　backdoor　and　adversarial　attacks　and　observe　two　intriguing　phenomena　when　applying　adversarial　attacks　on　an　infected　model　implanted　with　backdoors:　1)　the　sample　is　harder　to　be　turned　into　an　AE　when　the　trigger　is　presented;　2)　the　AEs　generated　from　backdoor　samples　are　highly　likely　to　be　predicted　as　its　true　labels.　Inspired　by　these　observations,　we　proposed　a　novel　backdoor　defense　method,　dubbed　Adversarial-Inspired　Backdoor　Defense　(AIBD),　to　isolate　the　backdoor　samples　by　leveraging　a　progressive　top-q　scheme　and　break　the　correlation　between　backdoor　samples　and　their　target　labels　using　adversarial　labels.　Through　extensive　experiments　on　various　datasets　against　six　state-of-the-art　backdoor　attacks,　the　AIBD-trained　models　on　poisoned　data　demonstrate　superior　performance　over　the　existing　defense　methods.