With　the　prosperous　development　of　Internet　of　Things　(IoT),　IoT　devices　have　been　deployed　in　various　applications,　which　generates　large　volume　of　image　data　to　trace　and　record　the　users＇　behaviors,　resulting　in　better　IoT　services.　To　accurately　analyze　these　huge　data　to　further　improve　users＇　experience　on　IoT　services,　deep　neural　networks　(DNNs)　are　gaining　more　attention　and　have　become　increasingly　popular.　However,　recent　studies　have　shown　that　DNN　models　are　vulnerable　to　adversarial　attacks,　which　leads　to　the　risk　of　applications　in　practice.　Previous　works　are　devoted　to　extract　invariant　features　from　the　content　circled　by　edges　in　images,　while　such　features　cannot　efficiently　deal　with　the　adversarial　effect.　In　this　work,　we　first　study　this　problem　from　a　new　angle　by　exploring　the　edge　feature　information,　which　is　intractable　to　be　influenced　by　adversarial　attacks　demonstrated　by　our　empirical　analysis.　Based　on　this,　we　propose　a　novel　edge　feature-enhanced　defense　approach　called　Defed　which　incorporates　edge　feature　information　into　denoised　network　to　defend　against　various　adversarial　attacks　in　image　area.　For　the　training　phase,　we　only　add　benign　images　as　the　input　and　exert　Gaussian　noise　to　substitute　the　adversarial　attacks　to　mitigate　the　dependency　of　models　on　specific　adversarial　attacks.　For　inference,　we　design　a　combination　of　multiple　Defeds　trained　by　different　Gaussian　noise　levels　and　deploy　confidence　intervals　to　judge　whether　an　image　is　adversarial　or　not.　Experiments　over　real-world　data　sets　on　image　classification　demonstrate　the　efficacy　and　superiority　compared　to　the　state-of-the-art　defense　approaches.