期刊
SIGNAL PROCESSING
卷 155, 期 -, 页码 218-232出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.sigpro.2018.10.001
关键词
Image encryption; Parallel compressive sensing; Visually secure cipher image; Discrete wavelet transform; Integer discrete wavelet transform
资金
- National Natural Science Foundation of China [61572089, 61633005]
- Chongqing Research Program of Basic Research and Frontier Technology [cstc2017jcyjBX0008]
- Graduate Student Research and Innovation Foundation of Chongqing [CYB17026]
- Chongqing Postgraduate Education Reform Project [yjg183018]
- Chongqing University Postgraduate Education Reform Project [cquyjg18219]
- Fundamental Research Funds for the Central Universities [106112017CDJQJ188830, 106112017CDJXY180005]
- Research Program of Chongqing Education Commission [JK15012027, JK1601225]
It is generally recognized that encrypting an original image into meaningless cipher image is an ideal method to protect image information. However, during transmission, the meaningless cipher image would draw attention and thus attract attacks. Recently, compressive sensing (CS) and carrier images have been utilized by Chai et al. to construct a novel image encryption scheme with visual security. However, in this scheme, some extra transmission is required for possible decryption besides the encrypted image. Moreover, the imperceptibility of the cipher image can be further improved and the recovered image quality would be severely degraded if unsuitable carrier images are selected. In this paper, we design a visually secure encryption scheme by using the parallel compressive sensing (PCS) counter mode and embedding technique. In order to achieve higher security level, Logistic-Tent system and 3-D Cat map are introduced to construct the measurement matrices and to disturb the order of the embedded information, respectively. Furthermore, experimental results demonstrate that the cipher image exhibits superior imperceptibility and the recovered image possesses more satisfactory quality, which is independent of the carrier image. (C) 2018 Elsevier B.V. All rights reserved.
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