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Image retrieval systems help users to browse and search among extensiveimages in real-time. With the rise of cloud computing, retrieval tasks areusually outsourced to cloud servers. However, the cloud scenario brings adaunting challenge of privacy protection as cloud servers cannot be fullytrusted. To this end, image-encryption-based privacy-preserving image retrievalschemes have been developed, which first extract features from cipher-images,and then build retrieval models based on these features. Yet, most existingapproaches extract shallow features and design trivial retrieval models,resulting in insufficient expressiveness for the cipher-images. In this paper,we propose a novel paradigm named Encrypted Vision Transformer (EViT), whichadvances the discriminative representations capability of cipher-images. First,in order to capture comprehensive ruled information, we extract multi-levellocal length sequence and global Huffman-code frequency features from thecipher-images which are encrypted by stream cipher during JPEG compressionprocess. Second, we design the Vision Transformer-based retrieval model tocouple with the multi-level features, and propose two adaptive dataaugmentation methods to improve representation power of the retrieval model.Our proposal can be easily adapted to unsupervised and supervised settings viaself-supervised contrastive learning manner. Extensive experiments reveal thatEViT achieves both excellent encryption and retrieval performance,outperforming current schemes in terms of retrieval accuracy by large marginswhile protecting image privacy effectively. Code is publicly available at\url{https://github.com/onlinehuazai/EViT}.

EViT: Privacy-Preserving Image Retrieval via Encrypted Vision Transformer in Cloud Computing