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QDCT Encoding-Based Retrieval for Encrypted JPEG Images

Qiuju Ji1, Peipeng Yu1, Zhihua Xia1, *

1 School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China.

* Corresponding Author: Zhihua Xia. Email: email.

Journal on Big Data 2020, 2(1), 33-51. https://doi.org/10.32604/jbd.2020.01004

Abstract

Aprivacy-preserving search model for JPEG images is proposed in paper, which uses the bag-of-encrypted-words based on QDCT (Quaternion Discrete Cosine Transform) encoding. The JPEG image is obtained by a series of steps such as DCT (Discrete Cosine Transform) transformation, quantization, entropy coding, etc. In this paper, we firstly transform the images from spatial domain into quaternion domain. By analyzing the algebraic relationship between QDCT and DCT, a QDCT quantization table and QDTC coding for color images are proposed. Then the compressed image data is encrypted after the steps of block permutation, intra-block permutation, single table substitution and stream cipher. At last, the similarity between original image and query image can be measured by the Manhattan distance, which is calculated by two feature vectors with the model of bagof -words on the cloud server side. The outcome shows good performance in security attack and retrieval accuracy.

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APA Style
Ji, Q., Yu, P., Xia, Z. (2020). QDCT encoding-based retrieval for encrypted JPEG images. Journal on Big Data, 2(1), 33-51. https://doi.org/10.32604/jbd.2020.01004
Vancouver Style
Ji Q, Yu P, Xia Z. QDCT encoding-based retrieval for encrypted JPEG images. J Big Data . 2020;2(1):33-51 https://doi.org/10.32604/jbd.2020.01004
IEEE Style
Q. Ji, P. Yu, and Z. Xia "QDCT Encoding-Based Retrieval for Encrypted JPEG Images," J. Big Data , vol. 2, no. 1, pp. 33-51. 2020. https://doi.org/10.32604/jbd.2020.01004



cc 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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