文献类型：期刊文献

英文题名：Novel Meaningful Image Encryption Based on Block Compressive Sensing

作者：Pan, Chen[1];Ye, Guodong[1];Huang, Xiaoling[1];Zhou, Junwei[2,3]

机构：[1]Guangdong Ocean Univ, Fac Math & Comp Sci, Zhanjiang 524088, Peoples R China;[2]Wuhan Univ Technol, Sch Comp Sci & Technol, Wuhan 430070, Hubei, Peoples R China;[3]Penn State Univ, University Pk, PA 16802 USA

年份：2019

卷号：2019

外文期刊名：SECURITY AND COMMUNICATION NETWORKS

收录：SCI-EXPANDED(收录号：WOS:000501767600002)、、EI(收录号：20195107873912)、Scopus(收录号：2-s2.0-85076516529)、WOS

基金：This work was supported in part by the National Natural Science Foundations of China (nos. 61972103 and 61702116), the Natural Science Foundation of Guangdong Province of China (no. 2019A1515011361), the Science and Technology Planning Project of Guangdong Province of China (no. 2017A010101025), the Program for Scientific Research Start-up Funds of Guangdong Ocean University of China (no. R17037), the Special Funding Program for Excellent Young Scholars of Guangdong Ocean University of China (no. HDYQ2017006), and the Project of Enhancing School with Innovation of Guangdong Ocean University of China (Q18306).

语种：英文

外文关键词：Compressed sensing - Cryptography - Discrete cosine transforms - Discrete wavelet transforms

外文摘要：This paper proposes a new image compression-encryption algorithm based on a meaningful image encryption framework. In block compressed sensing, the plain image is divided into blocks, and subsequently, each block is rendered sparse. The zigzag scrambling method is used to scramble pixel positions in all the blocks, and subsequently, dimension reduction is undertaken via compressive sensing. To ensure the robustness and security of our algorithm and the convenience of subsequent embedding operations, each block is merged, quantized, and disturbed again to obtain the secret image. In particular, landscape paintings have a characteristic hazy beauty, and secret images can be camouflaged in them to some extent. For this reason, in this paper, a landscape painting is selected as the carrier image. After a 2-level discrete wavelet transform (DWT) of the carrier image, the low-frequency and high-frequency coefficients obtained are further subjected to a discrete cosine transform (DCT). The DCT is simultaneously applied to the secret image as well to split it. Next, it is embedded into the DCT coefficients of the low-frequency and high-frequency components, respectively. Finally, the encrypted image is obtained. The experimental results show that, under the same compression ratio, the proposed image compression-encryption algorithm has better reconstruction effect, stronger security and imperceptibility, lower computational complexity, shorter time consumption, and lesser storage space requirements than the existing ones.