Intelligent and Secure Content-Based Image Retrieval for Mobile Users

Abstract

With the tremendous growth of smart mobile devices, the Content-Based Image Retrieval (CBIR) becomes popular and has great market potentials. Secure image retrieval has attracted considerable interests recently due to users' security concerns. However, it still suffers from the challenges of relieving mobile devices of excessive computation burdens, such as data encryption, feature extraction, and image similarity scoring. In this paper, we propose and implement an IND-CPA secure CBIR framework that performs image retrieval on the cloud without the user's constant interaction. A pre-trained deep CNN model, i.e., VGG-16, is used to extract the deep features of an image. The information about the neural network is strictly concealed by utilizing the lattice-based homomorphic scheme. We implement a real number computation mechanism and a divide-and-conquer CNN evaluation protocol to enable our framework to securely and efficiently evaluate the deep CNN with a large number of inputs. We further propose a secure image similarity scoring protocol, which enables the cloud servers to compare two images without knowing any information about their deep features. The comprehensive experimental results show that our framework is efficient and accurate.