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Secure Content Based Image Retrieval Scheme Based on Deep Hashing and Searchable Encryption

Zhen Wang, Qiu-yu Zhang*, Ling-tao Meng, Yi-lin Liu

School of Computer and Communication, Lanzhou University of Technology, Lanzhou, 730050, China

* Corresponding Author: Qiu-yu Zhang. Email:

Computers, Materials & Continua 2023, 75(3), 6161-6184.


To solve the problem that the existing ciphertext domain image retrieval system is challenging to balance security, retrieval efficiency, and retrieval accuracy. This research suggests a searchable encryption and deep hashing-based secure image retrieval technique that extracts more expressive image features and constructs a secure, searchable encryption scheme. First, a deep learning framework based on residual network and transfer learning model is designed to extract more representative image deep features. Secondly, the central similarity is used to quantify and construct the deep hash sequence of features. The Paillier homomorphic encryption encrypts the deep hash sequence to build a high-security and low-complexity searchable index. Finally, according to the additive homomorphic property of Paillier homomorphic encryption, a similarity measurement method suitable for computing in the retrieval system’s security is ensured by the encrypted domain. The experimental results, which were obtained on Web Image Database from the National University of Singapore (NUS-WIDE), Microsoft Common Objects in Context (MS COCO), and ImageNet data sets, demonstrate the system’s robust security and precise retrieval, the proposed scheme can achieve efficient image retrieval without revealing user privacy. The retrieval accuracy is improved by at least 37% compared to traditional hashing schemes. At the same time, the retrieval time is saved by at least 9.7% compared to the latest deep hashing schemes.


Cite This Article

Z. Wang, Q. Zhang, L. Meng and Y. Liu, "Secure content based image retrieval scheme based on deep hashing and searchable encryption," Computers, Materials & Continua, vol. 75, no.3, pp. 6161–6184, 2023.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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