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.INDEX TERMS Content-based image retrieval, convolutional neural network (CNN), lattice-based homomorphic scheme.

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Content-based image retrieval (CBIR) with deep neural networks (DNNs) on the cloud has tremendous business and technical advantages to handle large-scale image repositories. However, cloud-based CBIR service raises challenges in image data and DNN model security. Typically, users who wish to request CBIR services on the cloud require their input images remaining confidential. On the other hand, image owners may intentionally (or unintentionally) upload adversarial examples to the cloud servers, which potentially leads to the misbehavior of CBIR services. Generative Adversarial Networks (GANs) can be utilized to defense against such malicious behavior. However, the GANs model, if not well protected, can be easily abused by the cloud to reconstruct the users’ original image data. In this paper, we focus on the problem of secure generative model evaluation and secure gradient descent (GD) computation in GANs. We propose two secure generative model evaluation algorithms and two secure minimizer protocols. Furthermore, we propose and implement Sec-Defense-Gan, a secure image reconstruction framework which can keep the image data, the generative model details and corresponding outputs confidential from the cloud. Finally, We carried out a set of benchmarks over two public available image datasets to show the performance and correctness of Sec-Defense-Gan.