With　the　development　of　outsourcing　services,　users　with　limited　resources　tend　to　store　encrypted　images　on　remote　servers　and　search　them　anytime　and　anywhere.　However,　existing　encrypted　image　search　schemes　are　proposed　for　cloud　computing　scenarios,　and　have　some　defects,　such　as　excessive　bandwidth　resource　consumption　or　network　delay,　which　are　not　suitable　for　Internet　of　Things　(IoT)　devices　in　edge　computing　environment.　Therefore,　we　propose　a　secure　and　verifiable　multikey　image　search　(SVMIS)　scheme　in　cloud-assisted　edge　computing.　First,　the　pretrained　convolutional　neural　network　model　is　employed　to　extract　image　feature　vectors　to　improve　search　accuracy.　Then,　a　key　distribution　protocol　is　designed　to　convert　the　encrypted　indexes　of　different　owners,　and　a　transformation　key　list　is　constructed　to　support　the　multikey　setting　in　edge　computing.　Next,　the　learning　with　errors　based　secure　k-nearest　neighbor　algorithm　is　used　to　encrypt　feature　vectors　to　improve　security.　Finally,　the　Merkle　hash　tree　is　utilized　to　check　the　correctness　of　search　results　returned　by　edge　servers.　Theoretical　analysis　and　extensive　experiments　using　a　real-world　dataset　evaluate　the　security　and　effectiveness　of　SVMIS.