Visually meaningful image encryption based on twin physically unclonable functions and compressed sensing

doi:10.11996/JG.j.2095-302X.2024050998

Abstract

Abstract:

The visually meaningful image encryption (VMIE) scheme has attracted the attention from researchers due to its ability to conceal encrypted images in plaintext images without raising suspicion from attackers. In order to enhance the performance of the system in terms of capacity and security, many VMIE schemes based on compressed sensing have been proposed. However, it remains difficult to solve the key management problem effectively in these schemes. Therefore, a VMIE scheme based on twin physically unclonable functions and compressed sensing was proposed. First, the complete key seed was embedded in the carrier image to achieve public network key exchange while avoiding additional communication consumption. With the key seed being encrypted and encoded, both security and robustness were guaranteed. Subsequently, the key seed was iteratively extended using a hash algorithm to generate the key stream. The experimental results demonstrated that the scheme can achieve the balance of security, embedding capacity, and robustness.

Key words: visually meaningful image encryption, compressive sensing, physical unclonable function, hash chain

CLC Number:

TP309.7

GAO Xianwei, GUO Weikai, CHENG Yixuan, YUAN Ye, SUO Zhufeng. Visually meaningful image encryption based on twin physically unclonable functions and compressed sensing[J]. Journal of Graphics, 2024, 45(5): 998-1007.

Figures/Tables 12

References 25

[1]	BARAKABITZE A A, BARMAN N, AHMAD A, et al. QoE management of multimedia streaming services in future networks: a tutorial and survey[J]. IEEE Communications Surveys & Tutorials, 2020, 22(1): 526-565.
[2]	BROZ M. How many pictures are there (2024): statistics, trends, and forecasts[EB/OL]. [2024-02-10]. https://photutorial.com/photos-statistics/.
[3]	芮杰, 杭后俊. 基于超混沌系统的明文关联图像加密算法[J]. 图学学报, 2020, 41(6): 917-921.
	RUI J, HANG H J. Plaintext correlation image encryption algorithm based on hyper-chaotic system[J]. Journal of Graphics, 2020, 41(6): 917-921 (in Chinese).
[4]	蒋东华, 刘立东, 王兴元, 等. 基于细胞神经网络和并行压缩感知的图像加密算法[J]. 图学学报, 2021, 42(6): 891-898.
	JIANG D H, LIU L D, WANG X Y, et al. Image encryption algorithm based on cellular neural network and parallel compressed sensing[J]. Journal of Graphics, 2021, 42(6): 891-898 (in Chinese).
[5]	YAMAC M, AHISHALI M, PASSALIS N, et al. Multi-level reversible data anonymization via compressive sensing and data hiding[J]. IEEE Transactions on Information Forensics and Security, 2021, 16: 1014-1028.
[6]	郭永宁, 孙树亮. 基于混沌映射和DNA序列的图像加密[J]. 图学学报, 2017, 38(6): 837-842. DOI
	GUO Y N, SUN S L. Image encryption based on chaotic map and DNA sequence operations[J]. Journal of Graphics, 2017, 38(6): 837-842 (in Chinese). DOI
[7]	SUO Z F, DONG Y H, TONG F H, et al. Semiconductor superlattice physical unclonable function based two-dimensional compressive sensing cryptosystem and its application to image encryption[J]. Information Sciences, 2022, 618: 227-252.
[8]	BAO L, ZHOU Y C. Image encryption: generating visually meaningful encrypted images[J]. Information Sciences, 2015, 324: 197-207.
[9]	YANG Y, CHENG M, DING Y Q, et al. A visually meaningful image encryption scheme based on lossless compression SPIHT coding[J]. IEEE Transactions on Services Computing, 2023, 16(4): 2387-2401.
[10]	CHAI X L, GAN Z H, CHEN Y R, et al. A visually secure image encryption scheme based on compressive sensing[J]. Signal Processing, 2017, 134: 35-51.
[11]	CHAI X L, WU H Y, GAN Z H, et al. An efficient visually meaningful image compression and encryption scheme based on compressive sensing and dynamic LSB embedding[J]. Optics and Lasers in Engineering, 2020, 124: 105837.
[12]	WANG H, XIAO D, LI M, et al. A visually secure image encryption scheme based on parallel compressive sensing[J]. Signal Processing, 2019, 155: 218-232.
[13]	WANG X Y, LIU C, JIANG D H. A novel triple-image encryption and hiding algorithm based on chaos, compressive sensing and 3D DCT[J]. Information Sciences, 2021, 574: 505-527.
[14]	ZHU L Y, SONG H S, ZHANG X, et al. A robust meaningful image encryption scheme based on block compressive sensing and SVD embedding[J]. Signal Processing, 2020, 175: 107629.
[15]	CHAI X L, WU H Y, GAN Z H, et al. An efficient approach for encrypting double color images into a visually meaningful cipher image using 2D compressive sensing[J]. Information Sciences, 2021, 556: 305-340.
[16]	KER A D, BAS P, BÖHME R, et al. Moving steganography and steganalysis from the laboratory into the real world[C]// The 1st ACM Workshop on Information Hiding and Multimedia Security. New York: ACM, 2013: 45-58.
[17]	JIANG D H, LIU L D, ZHU L Y, et al. Adaptive embedding: a novel meaningful image encryption scheme based on parallel compressive sensing and slant transform[J]. Signal Processing, 2021, 188: 108220.
[18]	PREISHUBER M, HÜTTER T, KATZENBEISSER S, et al. Depreciating motivation and empirical security analysis of chaos-based image and video encryption[J]. IEEE Transactions on Information Forensics and Security, 2018, 13(9): 2137-2150.
[19]	PAPPU R, RECHT B, TAYLOR J, et al. Physical one-way functions[J]. Science, 2002, 297(5589): 2026-2030. PMID
[20]	GAO Y S, AL-SARAWI S F, ABBOTT D. Physical unclonable functions[J]. Nature Electronics, 2020, 3(2): 81-91.
[21]	MOMPO E, RUIZ-GARCIA M, CARRETERO M, et al. Coherence resonance and stochastic resonance in an excitable semiconductor superlattice[J]. Physical Review Letters, 2018, 121(8): 086805.
[22]	WU H, YIN Z Z, TONG X H, et al. An experimental demonstration of long-haul public-channel key distribution using matched superlattice physical unclonable function pairs[J]. Science Bulletin, 2020, 65(11): 879-882. DOI PMID
[23]	DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306.
[24]	DAMGÅRD I B. A design principle for hash functions[C]// Advances in Cryptology - CRYPTO ’89. Cham:Springer, 1989: 416-427.
[25]	XU B, XIE Z L, ZHANG Z, et al. Joint compression and encryption of distributed sources based on wavelet transform and semi-tensor product compressed sensing[J]. IEEE Sensors Journal, 2022, 22(16): 16451-16463.

图像大小	明文图像	PSNRdec/dB	MSSIM	载体图像	PSNRste/dB	MSSIMste
512×512	Parrots	36.081 4	0.907 9	Lena	36.414 4	0.992 7
	Monarch	35.679 9	0.945 4
	Airplane	34.682 5	0.914 2
1024×1024	Girlface	38.592 0	0.969 4	Moon	36.391 3	0.994 1
	Zelda	38.294 6	0.973 4
	Pepper	37.671 7	0.967 6

图像大小	明文图像	PSNRdec/dB	MSSIM	载体图像	PSNRste/dB	MSSIMste
512×512	Parrots	36.081 4	0.907 9	Lena	36.414 4	0.992 7
	Monarch	35.679 9	0.945 4
	Airplane	34.682 5	0.914 2
1024×1024	Girlface	38.592 0	0.969 4	Moon	36.391 3	0.994 1
	Zelda	38.294 6	0.973 4
	Pepper	37.671 7	0.967 6

Clipping	Code rate
	1/16		1/32		1/64
	512×512	1024×1024	512×512	1024×1024	512×512	1024×1024
1/3	×	×	×	×	×	√
1/4	×	×	×	√	√	√
1/8	×	√	√	√	√	√

Clipping	Code rate
	1/16		1/32		1/64
	512×512	1024×1024	512×512	1024×1024	512×512	1024×1024
1/3	×	×	×	×	×	√
1/4	×	×	×	√	√	√
1/8	×	√	√	√	√	√

噪声类型	攻击强度/%	文献[11]	文献[14]	本文算法
SPN	0.000 1	33.44	31.56	36.057 57
	0.003 0	33.44	30.22	34.054 70
	0.005 0	33.44	30.02	33.127 50
GN	0.000 1	14.75	25.13	31.867 80
	0.003 0	9.35	19.41	29.768 40
	0.005 0	8.54	17.78	29.416 40
SN	0.000 1	33.44	31.56	36.191 50
	0.003 0	22.50	27.68	32.748 30
	0.005 0	17.30	24.39	30.609 00