基于二维压缩感知的有意义图像加密算法研究

doi:10.11959/j.issn.1000-436x.2022101

Abstract

Abstract:

Aiming at the problems that the existing compressive sensing-based meaningful image encryption algorithms have low visual security of the encrypted images and poor quality of the reconstructed images, a 2-dimensional compressive sensing-based (2DCS) meaningful image encryption algorithm was proposed.Firstly, a chaotic pseudo-random sequence generation method associated with plaintext was designed, and the global random permutation and grayscale transformation operations were used for pre-encryption to improve the decrypted image quality.The generated pre-encrypted image was used as the input for 2DCS, and the secret image was generated after the compression encryption and quantization operations.Secondly, the relationship between the hidden and the modified data was considered, and an adaptive embedding method was adopted to modify the carrier coefficient values to improve the visual security of meaningful encrypted image.Finally, the 2-dimensional projection gradient reconstruction method was adopted to decompress and decrypt to obtain the decrypted image.The experimental results show that, compared with the existing algorithms, the proposed algorithm not only improves the visual security of encrypted image and the quality of reconstructed image, but also can resist noise and cropping attacks.

Key words: image encryption, 2-dimensional compressive sensing, meaningful image encryption, embedding technology

CLC Number:

TP309.7

Hua REN, Shaozhang NIU, Ruyong REN, Zhen YUE. Research on meaningful image encryption algorithm based on 2-dimensional compressive sensing[J]. Journal on Communications, 2022, 43(5): 45-57.

Figures/Tables 14

References 27

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图像	方向	秘密图像	载体图像	加密图像
	水平	0.031 4	0.944 0	0.948 0
Parrots	垂直	0.011 1	0.971 3	0.971 6
	对角线	0.012 4	0.915 7	0.931 4
	水平	-0.054 5	0.947 9	0.939 0
Monarch	垂直	0.109 0	0.974 8	0.965 5
	对角线	0.016 9	0.928 7	0.911 0
	水平	0.072 9	0.943 3	0.944 1
Camera	垂直	0.061 5	0.971 0	0.972 1
	对角线	-0.013 1	0.917 1	0.923 2
	水平	-0.029 4	0.941 8	0.937 7
Boats	垂直	0.058 6	0.969 9	0.969 3
	对角线	0.022 9	0.920 9	0.920 7
	水平	0.093 3	0.944 1	0.944 5
Barbara	垂直	0.034 7	0.968 9	0.970 1
	对角线	-0.007 6	0.920 0	0.924 4

图像	明文图像	载体图像	加密图像	重构图像
Parrots	7.414 1	7.444 2	7.456 1	7.525 4
Monarch	7.471 6	7.444 2	7.455 0	7.572 7
Camera	7.009 7	7.444 2	7.455 9	7.046 4
Boats	7.145 6	7.444 2	7.455 8	7.403 1
Barbara	7.525 2	7.444 2	7.455 5	7.563 6

图像	1 bit像素改变		2 bit像素改变
图像	NPCR	UACI	NPCR	UACI
Parrots	83.28%	0.76%	83.71%	0.76%
Monarch	83.48%	0.76%	83.11%	0.76%
Camera	83.79%	0.76%	83.87%	0.77%
Boats	83.59%	0.76%	83.36%	0.76%
Barbara	83.55%	0.76%	83.54%	0.76%

明文图像	载体图像	文献[12]算法	文献[13]算法	文献[15]算法	文献[17]算法	文献[20]算法	本文算法
Len	Peppers	32.351 3	34.513 4	31.798 6	35.143 5	41.667 3	42.320 7
Jet	Baboon	37.105 8	35.156 7	32.597 6	32.456 5	41.505 9	42.325 4
Girl	Goldhill	36.112 5	34.254 3	32.064 7	32.224 3	41.601 6	42.323 4
Barbara	Bridge	35.562 9	36.113 4	31.739 7	33.435 5	41.490 3	42.249 3
平均	—	35.283 1	35.009 5	32.050 2	33.315 0	41.566 3	42.304 7

明文图像	载体图像	文献[12]算法	文献[13]算法	文献[15]算法	文献[17]算法	文献[20]算法	本文算法
Lena	Peppers	0.925 7	0.995 2	0.990 3	0.991 8	0.996 7	0.998 3
Jet	Baboon	0.983 3	0.995 6	0.995 5	0.979 7	0.997 6	0.998 3
Girl	Goldhill	0.966 6	0.997 3	0.994 2	0.964 1	0.997 2	0.997 5
Barbara	Bridge	0.978 3	0.998 2	0.994 6	0.988 9	0.998 6	0.999 4
平均	—	0.963 5	0.996 6	0.993 7	0.981 1	0.997 5	0.998 4

Research on meaningful image encryption algorithm based on 2-dimensional compressive sensing

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明文图像	载体图像	文献[12]算法		文献[13]算法		文献[15]算法		文献[17]算法		文献[20]算法		本文算法
明文图像	载体图像	PSNR/dB	MSSIM	PSNR/dB	MSSIM	PSNR/dB	MSSIM	PSNR/dB	MSSIM	PSNR/dB	MSSIM	PSNR/dB	MSSIM
	Lena （512像素× 512像素）	28.443 5	0.812 8	28.553 4	0.993 2	27.123 2	0.814 5	32.118 5	0.867 0	35.778 4	0.985 8	36.145 0	0.986 1
Barbara （512像素× 512像素）	Boats （512像素× 512像素）	28.443 5	0.812 8	28.553 4	0.993 2	26.987 5	0.801 6	31.672 1	0.828 9	35.753 8	0.985 8	36.279 5	0.986 3
	Jet （512像素× 512像素）	28.443 5	0.812 8	28.553 4	0.993 2	26.714 5	0.793 4	31.982 1	0.896 7	35.103 1	0.983 4	35.894 6	0.985 3
	Peppers （512像素× 512像素）	28.443 5	0.812 8	28.553 4	0.993 2	27.014 5	0.803 4	29.960 2	0.784 5	35.485 4	0.984 1	35.969 1	0.985 5