Searchable Encrypted Image Retrieval Based on Multi-Feature Adaptive Late-Fusion

Wentao Ma, Jiaohua Qin, Xuyu Xiang, Yun Tan and Zhibin He
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Wentao Ma: College of Computer Science and Information Technology, Central South University of Forestry & Technology, Changsha 410004, China
Jiaohua Qin: College of Computer Science and Information Technology, Central South University of Forestry & Technology, Changsha 410004, China
Xuyu Xiang: College of Computer Science and Information Technology, Central South University of Forestry & Technology, Changsha 410004, China
Yun Tan: College of Computer Science and Information Technology, Central South University of Forestry & Technology, Changsha 410004, China
Zhibin He: College of Computer Science and Information Technology, Central South University of Forestry & Technology, Changsha 410004, China

Mathematics, 2020, vol. 8, issue 6, 1-15

Abstract: Recently, searchable encrypted image retrieval in a cloud environment has been widely studied. However, the inappropriate encryption mechanism and single feature description make it hard to achieve the expected effects. Therefore, a major challenge of encrypted image retrieval is how to extract and fuse multiple efficient features to improve performance. Towards this end, this paper proposes a searchable encrypted image retrieval based on multi-feature adaptive late-fusion in a cloud environment. Firstly, the image encryption is completed by designing the encryption function in an RGB color channel, bit plane and pixel position of the image. Secondly, the encrypted images are uploaded to the cloud server and the convolutional neural network (CNN) is fine-tuned to build a semantic feature extractor. Then, low-level features and semantic features are extracted. Finally, the similarity score curves of each feature are calculated, and adaptive late-fusion is performed by the area under the curve. A large number of experiments on public dateset are used to validate the effectiveness of our method.

Keywords: searchable encryption; multi-feature adaptive late-fusion; convolutional neural network (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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