Secure Image Retrieval Based on Deep Learning in Cloud Computing
Sijie Li, Xiehua Li · 2023
The rapid growth of multimedia data and the limited storage and computing capacity of local devices motivate the outsourcing service of cloud storage. For the outsourced images, users usually encrypt them to protect their privacy and require the ability to retrieve them later. Therefore, how effectively managing and retrieving massive encrypted images in cloud servers becomes a challenging problem. In this paper, we propose a deep learning-based image retrieval scheme for cloud computing. Firstly, we use a Convolutional Neural Network (CNN) to extract feature descriptors and represent each image as two parts: image category and image feature. Then, we construct an encrypted tree-based index structure to improve retrieval efficiency. At the same time, we use the Learning With Errors (LWE)-based secure k-Nearest Neighbor (kNN) algorithm and random matrix to protect the security of two parts of the descriptors. The feasibility of our scheme is proved through security analysis. Finally, we conduct empirical experiments on the Caltech256 image dataset, and the results show that our scheme can achieve high retrieval efficiency and accuracy while ensuring image security.