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Author:

Zhang, Yan (Zhang, Yan.) | Zhuo, Li (Zhuo, Li.) | Peng, Yuanfan (Peng, Yuanfan.) | Zhang, Jing (Zhang, Jing.) (Scholars:张菁)

Indexed by:

CPCI-S

Abstract:

In this paper, a secure image retrieval method for cloud computing is proposed, which can guarantee the security of the image content while not sacrificing the retrieval performance. The method is implemented based on CBIR (content-based image retrieval) framework. To perform the secure image retrieval, three kinds of low-level image features are firstly extracted from images, including color, texture and shape features. Locality preserving projections (LPP) is then applied to reduce the dimension of these image features. Secondly, image features are protected by the Paillier homomorphic encryption algorithm according to its homomorphic characteristics. Finally, similarity measurement is directly conducted between the encrypted features, and the top 12 similar images are treated as the retrieval results. The security and communication cost analysis of the proposed method are analyzed. Experimental results demonstrate that the proposed method can achieve the consistent retrieval results with the conventional CBIR method in the plaintext domain while providing adequate security.

Keyword:

similarity measurement homomorphic encryption CBIR (Content-Based Image Retrieval) image feature extraction

Author Community:

  • [ 1 ] [Zhang, Yan]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 2 ] [Zhuo, Li]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 3 ] [Peng, Yuanfan]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China
  • [ 4 ] [Zhang, Jing]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China

Reprint Author's Address:

  • [Zhang, Yan]Beijing Univ Technol, Signal & Informat Proc Lab, Beijing, Peoples R China

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Source :

2014 19TH INTERNATIONAL CONFERENCE ON DIGITAL SIGNAL PROCESSING (DSP)

ISSN: 1546-1874

Year: 2014

Page: 269-274

Language: English

Cited Count:

WoS CC Cited Count: 23

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 4

Online/Total:511/10592017
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