Deep　learning　has　achieved　great　success　in　many　fields,　such　as　image　classification　and　target　detection.　Adding　small　disturbance　which　is　hard　to　be　detected　by　the　human　eyes　to　original　images　can　make　the　neural　network　output　error　results　with　high　confidence.　An　image　after　adding　small　disturbance　is　an　adversarial　example.　The　existence　of　adversarial　examples　brings　a　huge　security　problem　to　deep　learning.　In　order　to　effectively　defend　against　adversarial　examples　attacks,　an　adversarial　example　defense　method　based　on　image　reconstruction　is　proposed　by　analyzing　the　existing　adversarial　examples　attack　methods　and　defense　methods.　Our　data　set　is　based　on　ImageNet　1k　data　set,　and　some　filtering　and　expansion　are　carried　out.　Four　attack　modes,　FGSM,　BIM,　DeepFool　and　C&W　are　selected　to　test　the　defense　method.　Based　on　the　EDSR　network,　multi-scale　feature　fusion　module　and　subspace　attention　module　are　added.　By　capturing　the　global　correlation　information　of　the　image,　the　disturbance　can　be　removed,　while　the　image　texture　details　can　be　better　preserved,　and　the　defense　performance　can　be　improved.　The　experimental　results　show　that　the　proposed　method　has　good　defense　effect.　©　2023　SPIE.