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
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 ( <inline-formula><tex-math notation="LaTeX">$Transpose$</tex-math></inline-formula> <inline-formula><tex-math notation="LaTeX">$MLP$</tex-math></inline-formula> ), 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.