The　increasing　awareness　in　privacy　has　partly　contributed　to　the　renewed　interest　in　privacy-preserving　encrypted　image　retrieval,　and　designing　for　outsourced　images　stored　on　cloud　servers,　etc.　However,　there　are　some　limitations　in　these　existing　schemes　such　as　low　retrieval　accuracy,　low　retrieval　efficiency,　and　less　efficient　result　verification　in　the　dynamic　setting.　Therefore,　in　this　paper　we　present　a　novel　Dynamic　Verifiable　Retrieval　over　Encrypted　Images　(DVREI)　scheme.　First,　a　pre-trained　Convolutional　Neural　Network　(CNN)　model　is　utilized　to　extract　image　features　to　improve　retrieval　accuracy.　Then,　an　encrypted　index　based　on　the　K-means　clustering　algorithm　is　designed　to　improve　retrieval　efficiency.　Finally,　a　dynamic　verification　tree　based　on　the　chameleon　hash　is　used　to　verify　the　correctness　of　the　retrieval　results　and　support　dynamic　updates.　We　theoretically　and　experimentally　evaluate　the　security　and　performance　of　DVREI　to　demonstrate　its　practicability.