A Privacy-Preserving JPEG Image Retrieval Scheme Using the Local Markov Feature and Bag-of-Words Model in Cloud Computing
Peipeng Yu, Jian Tang, Zhihua Xia, Zhetao Li, Jian Weng
Abstract
The development of cloud computing attracts a great deal of image owners to upload their images to the cloud server to save the local storage. But privacy becomes a great concern to the owner. A forthright way is to encrypt the images before uploading, which, however, would obstruct the efficient usage of image, such as the Content-Based Image Retrieval (CBIR). In this paper, we propose a privacy-preserving JPEG image retrieval scheme. The image content is protected by a specially-designed image encryption method, which is compatible to JPEG compression and makes no expansion to the final JPEG files. Then, the encrypted JPEG files are uploaded to the cloud, and the cloud can directly extract the features from the encrypted JPEG files for searching similar images. Specifically, big-blocks are first assembled with adjacent 8×8 discrete cosine transform (DCT) coefficient blocks. Then, the big-blocks are permuted and the binary code of DCT coefficients are substituted, so as to disturb the content of image. After receiving the encrypted images, local Markov features are extracted from the encrypted big-blocks, and then the Bag-Of-Words (BOW) model is applied to construct a feature vector with these local features to represent the image, so as to provide the CBIR service to image owner. Experimental results and security analysis demonstrate the retrieval performance and security of our scheme.