A Privacy-Preserving Image Retrieval Scheme Based on 16×16 DCT and Deep Learning

Zhixun Lu, Qihua Feng, Peiya Li, Kwok Tung Lo, Feiran Huang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

Abstract

In recent years, people tend to upload images to cloud servers, which provide storage and retrieval functions. To prevent users' privacy from leaking to the server, research on cipher-image retrieval has attracted much attention. This work presents a novel encrypted image retrieval method. With this scheme, we perform encryption during the JPEG compression process by applying 16×16 DCT (Discrete Cosine Transform) for blocks' transformation, followed by coefficients distribution and 8×8 blocks' permutation. For the retrieval part, when an encrypted query image is sent by an authorized user, the server extracts its DCT histograms as features and inputs them into our trained network model, which incorporates transpose Multilayer perceptron modules (TransposeTranspose MLPMLP), for retrieval. Experimental results show that our scheme, compared with related schemes, can improve the retrieval performance significantly, when ensuring compression friendliness and no feature information leakage. Moreover, our scheme enables cipher-image retrieval from multiple image owners.

Original languageEnglish
Article number10152501
Pages (from-to)3314-3325
Number of pages12
JournalIEEE Transactions on Cloud Computing
Volume11
Issue number3
DOIs
Publication statusPublished - 1 Jul 2023

Keywords

  • Cipher-image retrieval
  • JPEG
  • multilayer perceptron
  • neural network
  • privacy preservation

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications

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