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Applying encryption technology to image retrieval can ensure the security andprivacy of personal images. The related researches in this field have focusedon the organic combination of encryption algorithm and artificial featureextraction. Many existing encrypted image retrieval schemes cannot preventfeature leakage and file size increase or cannot achieve satisfied retrievalperformance. In this paper, A new end-to-end encrypted image retrieval schemeis presented. First, images are encrypted by using block rotation, neworthogonal transforms and block permutation during the JPEG compressionprocess. Second, we combine the triplet loss and the cross entropy loss totrain a network model, which contains gMLP modules, by end-to-end learning forextracting cipher-images' features. Compared with manual features extractionsuch as extracting color histogram, the end-to-end mechanism can economize onmanpower. Experimental results show that our scheme has good retrievalperformance, while can ensure compression friendly and no feature leakage.

A Privacy-Preserving and End-to-End-Based Encrypted Image Retrieval Scheme