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EViT: Privacy-Preserving Image Retrieval via Encrypted Vision Transformer in Cloud Computing by Feng, Qihua; Li, Peiya; Lu, Zhixun; Li, Chaozhuo; Wang, Zefang; Liu, Zhiquan; Duan, Chunhui; Huang, Feiran is a scholarly article available to read on EtoBox.

What is EViT: Privacy-Preserving Image Retrieval via Encrypted Vision Transformer in Cloud Computing about?

Image retrieval systems help users to browse and search among extensive images in real-time. With the rise of cloud computing, retrieval tasks are usually outsourced to cloud servers. However, the cloud scenario brings a daunting challenge of privacy protection as cloud servers cannot be fully trusted. To this end, image-encryption-based privacy-preserving image retrieval schemes have been developed, which first extract features from cipher-images, and then build retrieval models based on these features. Yet, most existing approaches 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), which advances the discriminative representations capability of cipher-images. First, in order to capture comprehensive ruled information, we extract multi-level local length sequence and global Huffman-code frequency features from the cipher-images which are encrypted by stream cipher during JPEG compression process. Second, we design the Vision Transformer-based retrieval model to couple with the multi-level features, and propose two adaptive

Author

Feng, Qihua; Li, Peiya; Lu, Zhixun; Li, Chaozhuo; Wang, Zefang; Liu, Zhiquan; Duan, Chunhui; Huang, Feiran

Published

2022

Language

EN