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An efficient meaningful double-image encryption algorithm based on parallel compressive sensing and FRFT embedding
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DOI：10.1007/s11042-023-14601-z.png)
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摘要
En 中文
The transmission of images via the Internet has grown exponentially in the past few decades. However, the Internet considered as an insecure method of information transmission may cause serious privacy issues. To overcome such potential security issues, a novel visually meaningful double-image encryption (VMDIE) algorithm conjugating quantum cellular neural network (QCNN), compressive sensing (CS) and fractional Fourier transform (FRFT) is proposed in this paper. First, the wavelet coefficients of two plain images are scrambled by the Fisher-Yates confusion algorithm, and compressed by key-controlled partial Hadamard matrix. The final meaningful cipher image is generated by embedding the encrypted images into a same-scale host image via the FRFT-based embedding approach. Besides, the eigenvalues of plain images are utilized to generate secret key streams to improve the ability of proposed VMDIE algorithm to withstand various plaintext attacks. Afterward, the plaintext eigenvalues are hidden into the alpha channel of meaningful cipher image under control of keys to relieve unnecessary storage space and transmission cost. Ultimately, simulation results and security analyses indicate that the proposed VMDIE algorithm is effective and can withstand multiple attacks.
Keyword:
Double-image compression and encryption
Visually meaningful encrypted image
Quantum cellular neural network
Compressive sensing
Fractional Fourier transform
期刊
IF:
3
论文数:
2.0W
被引数:
3.2W
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