RCAE_BFV: Retrieve Encrypted Images using Convolution AutoEncoder and BFV

Abstract

Content-Based Image Retrieval (CBIR) is an actual application in computer vision, which retrieves similar images from a database. Deep Learning (DL) is essential in many applications, including image retrieval applications. However, encryption techniques are used to protect data privacy because these data are vulnerable to being viewed by unauthorized parties while being transmitted over unsecured channels. This paper includes two parts for images retrieval. In the first part, features of all images of a Canadian Institute for Advanced Research CIFAR-10 dataset were extracted and stored on the Server-side. In the second part, the Brakerski/Fan-Vercauteren (BFV) homomorphic encryption scheme method for encrypting an image sent by the client-side. First, their decryption and image features are extracted depending on the trainer model when they arrive on the server-side. Then an extracted features are compared with stored features using the Cosine Distance method, and then the server encrypts the retrieved images and sends them to the client-side. Deep-learning results on plain images were 97% for classification and 96.7% for retriever images. At the same time, The National Institute of Standards and Technology (NIST ) test was used to check the security of BFV when applied to CIFAR-10 dataset. Index Terms— BFV, Convolution Autoencoder, Content-based image retrieval, Homomorphic encryption.