With　the　development　of　deep　learning　technology,　convolutional　neural　network　(CNN)　has　been　widely　used　in　many　fields　such　as　face　recognition,　automatic　driving,　biomedicine,　etc.,　replacing　human　beings　to　complete　complex　and　redundant　work,　which　brings　great　convenience　to　people＇s　lives.　However,　the　discovery　and　development　of　adversarial　examples　have　created　a　greater　threat　to　image　recognition.　In　this　paper,　we　propose　an　adaptive　image　adversarial　example　detection　method　based　on　class　activation　mapping,　which　utilizes　the　hot　zone　discovery　results　of　the　Grad-CAM　algorithm　to　perform　adaptive　noise　reduction　on　images　and　analyzes　the　differences　in　the　classification　results　of　images　before　and　after　the　noise　reduction　in　the　same　benchmark　network,　including　the　KL　dispersion,　the　label　change,　the　label　confidence,　etc.,　to　achieve　the　detection　of　adversarial　examples　on　the　ImageNet-1000　dataset.　The　experimental　results　show　that　the　algorithm　proposed　in　this　paper　achieves　better　detection　results,　and　the　F1　reaches　0.82　in　detecting　the　generated　FGSM　adversarial　examples　with　epsilon　=　0.3,　which　is　better　than　the　baseline　model.