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A robust meaningful image encryption scheme based on block compressive sensing and SVD embedding
Signal Processing ( IF 4.4 ) Pub Date : 2020-10-01 , DOI: 10.1016/j.sigpro.2020.107629
Liya Zhu , Huansheng Song , Xi Zhang , Maode Yan , Tao Zhang , Xiaoyan Wang , Juan Xu

Abstract In this paper, an efficient and robust meaningful image encryption (MIE) scheme is developed by combining block compressive sensing (BCS) and singular value decomposition (SVD) embedding. This work devotes to the balanced performance of security, compression, robustness and running efficiency. First, the plain image is divided equally and sparsely represented in discrete cosine transform (DCT) domain, and the coefficient vectors are confused using the coefficient random permutation (CRP) strategy and encrypted into a secret image by compressive sensing. Next, SVD embedding is employed to embed the secret image into a carrier image to create the final meaningful cipher image. In pursuit of superior security, the hyper-chaotic Lorenz system is utilized to generate the updated secret code streams for encryption and embedding with assistance from the counter mode. This scheme is suitable for processing the medium and large images in parallel. Additionally, it exhibits superior robustness and efficiency compared with existing related schemes. Simulation results and comprehensive performance analyses are presented to demonstrate the effectiveness, secrecy and robustness of the proposed scheme.

中文翻译:

基于块压缩感知和SVD嵌入的鲁棒有意义的图像加密方案

摘要 在本文中,通过结合块压缩感知 (BCS) 和奇异值分解 (SVD) 嵌入,开发了一种有效且稳健的有意义图像加密 (MIE) 方案。这项工作致力于安全性、压缩性、健壮性和运行效率的平衡性能。首先,在离散余弦变换 (DCT) 域中对普通图像进行均分和稀疏表示,使用系数随机排列 (CRP) 策略混淆系数向量,并通过压缩感知加密成秘密图像。接下来,使用 SVD 嵌入将秘密图像嵌入到载体图像中,以创建最终有意义的密码图像。追求卓越的安全性,超混沌洛伦兹系统用于生成更新的密码流,以在计数器模式的帮助下进行加密和嵌入。该方案适用于并行处理中大图像。此外,与现有的相关方案相比,它表现出卓越的鲁棒性和效率。给出了仿真结果和综合性能分析,以证明所提出方案的有效性、保密性和鲁棒性。
更新日期:2020-10-01
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