The　encrypted　image　retrieval　technique　allows　users　to　retrieve　images　in　an　encrypted　manner　without　decrypting　images.　However,　most　of　the　existing　schemes　still　are　vulnerable　to　security　threats　and　inefficiency,　caused　by　malicious　users　and　inefficient　feature　extraction　methods,　respectively.　To　this　end,　we　propose　a　traceable　encrypted　image　retrieval　in　the　multi-user　setting　in　this　article,　termed　as　MU-TEIR.　First,　MU-TEIR　employs　a　convolutional　neural　network　VGG16　to　extract　image　feature　vectors　and　calculate　the　mean　and　variance　of　the　feature　vectors　to　construct　the　index,　then　encrypts　index　with　the　distributed　two　trapdoors　public-key　cryptosystem.　After　that,　MU-TEIR　protects　image　content　by　encrypting　each　image　pixel　with　a　standard　stream　cipher.　Furthermore,　MU-TEIR　utilizes　a　watermark-based　mechanism　to　prevent　malicious　query　users　from　maliciously　distributing　images.　Detailed　security　analysis　shows　that　MU-TEIR　protects　the　outsourced　images　and　indexes　security　as　well　as　query　privacy,　and　can　track　malicious　users.　Experimental　results　verify　effectiveness　of　MU-TEIR.　©　2008-2012　IEEE.