广西师范大学学报（自然科学版） ›› 2022, Vol. 40 ›› Issue (5): 49-58.doi: 10.16088/j.issn.1001-6600.2022012003

• 综述 • 上一篇    下一篇

基于压缩感知的混沌图像加密研究综述

梁钰婷, 罗玉玲*, 张顺生   

  1. 广西师范大学 电子工程学院, 广西 桂林 541004
  • 收稿日期:2022-01-20 修回日期:2022-03-14 出版日期:2022-09-25 发布日期:2022-10-18
  • 通讯作者: 罗玉玲(1984—), 女, 湖北武汉人, 广西师范大学副教授, 博士。E-mail: yuling0616@gxnu.edu.cn
  • 基金资助:
    国家自然科学基金(61801131); 广西自然科学基金(2021JJA170174); 广西多源信息挖掘与安全重点实验室开放基金(19-A-03-02)

Review on Chaotic Image Encryption Based on Compressed Sensing

LIANG Yuting, LUO Yuling*, ZHANG Shunsheng   

  1. School of Electronic Engineering, Guangxi Normal University, Guilin Guangxi 541004, China
  • Received:2022-01-20 Revised:2022-03-14 Online:2022-09-25 Published:2022-10-18

PDF (PC)

734

摘要： 数字图像是当前网络环境下重要的信息载体,包含机密信息的图像一旦遭受恶意攻击和窃取,则极有可能出现信息安全问题。因此,对图像信息采取有效的加密保护迫在眉睫。基于压缩感知的混沌图像加密算法同时兼顾了图像数据安全和传输效率,具有很高的研究和应用价值。本综述首先介绍混沌理论、图像加密和压缩感知的基本原理;其次,分析基于压缩感知混沌图像加密中几种主要方法的特征及优势,总结当前的研究现状;最后讨论尚存的问题并展望未来的发展趋势。

关键词: 数字图像, 信息安全, 压缩感知, 混沌, 图像加密

Abstract: Digital image is an important information carrier in the current network environment. Once the image containing confidential information is attacked or stolen, it is very likely to bring about information security problems. Therefore, it is urgent to take effective protection schemes for image information. Takingimage data security and transmission efficiency into account,chaotic image encryption algorithm based on compressed sensinghas high value of research and application. This paper first introduces the basic principles of chaos theory, image encryption and compressed sensing. Then, the characteristics and advantages of several principal methods of chaotic image encryption based on compressed sensing are analyzed. In addition, the status quo of the current research is summarized. Finally, the remaining problems are discussed and the trend of future development is forecasted.

Key words: digital image, information security, compressed sensing, chaos, image encryption

中图分类号: 

  • TP309.7
[1]庄志本, 李军, 刘静漪,等. 基于新的五维多环多翼超混沌系统的图像加密算法[J]. 物理学报, 2020, 69(4): 040502. DOI:10.7498/aps.69.20191342.
[2]张雷, 陈川, 谭淇匀,等. 结合S盒与混沌映射的图像加密算法[J]. 北京邮电大学学报, 2021, 44(6): 40-47. DOI:10.13190/j.jbupt.2021-061.
[3]葛滨, 陈旭, 陈刚. 向量运算加速的超混沌图像加密算法[J]. 西安电子科技大学学报, 2021, 48(6): 187-196. DOI:10.19665/j.issn1001-2400.2021.06.023.
[4]李蓝航, 丘森辉, 王文仪,等. 基于混沌映射的彩色图像多层交互加密算法[J]. 广西师范大学学报(自然科学版), 2021, 39(6): 72-86. DOI:10.16088/j.issn.1001-6600.2020121603.
[5]石航, 王丽丹. 一种基于压缩感知和多维混沌系统的多过程图像加密方案[J]. 物理学报, 2019, 68(20): 200501. DOI:10.7498/aps.68.20190553.
[6]蒋东华, 朱礼亚, 沈子懿,等. 结合二维压缩感知和混沌映射的双图像视觉安全加密算法[J]. 西安交通大学学报, 2022, 56(2): 139-148.
[7]CHEN J X, ZHANG Y, QI L, et al. Exploiting chaos-based compressed sensing and cryptographic algorithm for image encryption and compression[J]. Optics & Laser Technology, 2018, 99: 238-248.
[8]DONOHO D L. Compressed sensing[J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289-1306. DOI:10.1109/TIT.2006.871582.
[9]LORENZ E N. Deterministic nonperiodic flow[J]. Journal of Atmospheric Sciences, 1963, 20(2): 130-141. DOI:10.1175/1520-0469(1963)020<0130:DNF>2.0.CO;2.
[10]MATTHEWS R. On the derivation of a “chaotic” encryption algorithm[J]. Cryptologia, 1989, 13(1): 29-42. DOI:10.1080/0161-118991863745.
[11]HABUTSU T, NISHIO Y, SASASE I, et al. A secret key cryptosystem by iterating a chaotic map[J]. Lecture Notes in Computer Science, 1991, 547(1): 127-140.
[12]FRIDRICH J. Symmetric ciphers based on two-dimensional chaotic maps[J]. International Journal of Bifurcation and Chaos, 1998, 8(6): 1259-1284. DOI:10.1142/S021812749800098X.
[13]HUA Z Y, ZHOU Y C, HUANG H J. Cosine-transform-based chaotic system for image encryption[J]. Information Sciences, 2019, 480: 403-419. DOI:10.1016/j.ins.2018.12.048.
[14]LUO Y L, CAO L C, QIU S H, et al. A chaotic map-control-based and the plain image-related cryptosystem[J]. Nonlinear Dynamics, 2016, 83(4): 2293-2310. DOI:10.1007/s11071-015-2481-7.
[15]班多晗, 吕鑫, 王鑫元. 基于一维混沌映射的高效图像加密算法[J]. 计算机科学, 2020, 47(4): 278-284. DOI:10.11896/jsjkx.190600059.
[16]田军锋, 彭静静, 左宪禹,等. 基于循环移位和多混沌映射的图像加密算法[J]. 计算机科学, 2020, 47(10): 327-331. DOI:10.11896/jsjkx.190800003.
[17]HUA Z Y, JIN F, XU B X, et al. 2D Logistic-sine-coupling map for image encryption[J]. Signal Processing, 2018, 149: 148-161.
[18]CHEN C, SUN K H, HE S B. An improved image encryption algorithm with finite computing precision[J]. Signal Processing, 2020, 168: 107340. DOI:10.1016/j.sigpro.2019.107340.
[19]LUO Y L, ZHOU R L, LIU J X, et al. An efficient and self-adapting colour-image encryption algorithm based on chaos and interactions among multiple layers[J]. Multimedia Tools and Applications, 2018, 77(20): 26191-26217. DOI:10.1007/s11042-018-5844-5.
[20]罗玉玲, 欧阳雪, 曹绿晨,等. 遗传模拟退火算法和混沌系统的图像加密方法[J]. 西安电子科技大学学报, 2019, 46(5): 171-179. DOI:10.19665/j.issn1001-2400.2019.05.024.
[21]LUO Y L, ZHOU R L, LIU J X. A novel image encryption scheme based on Kepler’s third law and random Hadamard transform[J]. Chinese Physics B, 2017, 26(12): 120504. DOI:10.1088/1674-1056/26/12/120504.
[22]LUO Y L, TANG S B, QIN X S, et al. A double-image encryption scheme based on amplitude-phase encoding and discrete complex random transformation[J]. IEEE Access, 2018, 6: 77740-77753. DOI:10.1109/ACCESS.2018.2884013.
[23]周红亮, 刘洪娟. 结合DNA编码的快速混沌图像加密算法[J]. 东北大学学报(自然科学版), 2021, 42(10): 1391-1399. DOI:10.12068/j.issn.1005-3026.2021.10.004.
[24]YUAN L G, ZHENG S, ALAM Z. Dynamics analysis and cryptographic application of fractional logistic map[J]. Nonlinear Dynamics, 2019, 96(1): 615-636. DOI:10.1007/s11071-019-04810-3.
[25]CHEN L P, YIN H, HUANG T W, et al. Chaos in fractional-order discrete neural networks with application to image encryption[J]. Neural Networks, 2020, 125: 174-184. DOI:10.1016/j.neunet.2020.02.008.
[26]刘思聪, 李春彪, 李泳新. 基于指数-余弦离散混沌映射的图像加密算法研究[J]. 电子与信息学报, 2022, 44(5): 1754-1762.
[27]RACHLIN Y, BARON R D. The secrecy of compressed sensing measurements[C]// 2008 46th Annual Allerton Conference on Communication, Control, and Computing. Piscataway, NJ: IEEE, 2008: 813-817. DOI: 10.1109/ALLERTON.2008.4797641.
[28]ZHANG L Y, WONG K W, ZHANG Y S, et al. Bi-level protected compressive sampling[J]. IEEE Transactions on Multimedia, 2016, 18(9): 1720-1732. DOI:10.1109/TMM.2016.2581593.
[29]HUANG H, HE X, XIANG Y, et al. A compression-diffusion-permutation strategy for securing image[J]. Signal Processing, 2018, 150: 183-190. DOI:10.1016/j.sigpro.2018.04.014.
[30]FANG H, VOROBYOV S A, JIANG H, et al. Permutation meets parallel compressed sensing: how to relax restricted isometry property for 2D sparse signals[J]. IEEE Transactions on Signal Processing, 2014, 62(1): 196-210. DOI:10.1109/TSP.2013.2284762.
[31]XU Q Y, SUN K H, HE S B, et al. An effective image encryption algorithm based on compressive sensing and 2D-SLIM[J]. Optics and Lasers in Engineering, 2020, 134: 106178. DOI:10.1016/j.optlaseng.2020.106178.
[32]CHAI X L, BI J Q, GAN Z H, et al. Color image compression and encryption scheme based on compressive sensing and double random encryption strategy[J]. Signal Processing, 2020, 176: 107684. DOI:10.1016/j.sigpro.2020.107684.
[33]GAN Z H, CHAI X L, ZHANG J T, et al. An effective image compression-encryption scheme based on compressive sensing (CS) and game of life (GOL)[J]. Neural Computing and Applications, 2020, 32(17): 14113-14141. DOI:10.1007/s00521-020-04808-8.
[34]GONG L H, QIU K D, DENG C Z, et al. An image compression and encryption algorithm based on chaotic system & compressive sensing[J]. Optics & Laser Technology, 2019, 115: 257-267. DOI:10.1016/j.optlastec.2019.01.039.
[35]XU Q Y, SUN K H, CAO C, et al. A fast image encryption algorithm based on compressive sensing and hyperchaotic map[J]. Optics and Lasers in Engineering, 2019, 121: 203-214. DOI:10.1016/j.optlaseng.2019.04.011.
[36]LU P, XU Z Y, LU X, et al. Digital image information encryption based on compressive sensing and double random-phase encoding technique[J]. Optik, 2013, 124(16): 2514-2518. DOI:10.1016/j.ijleo.2012.08.017.
[37]ZHOU N R, ZHANG A D, ZHENG F, et al. Novel image compression-encryption hybrid algorithm based on key-controlled measurement matrix in compressive sensing[J]. Optics & Laser Technology, 2014, 62: 152-160. DOI:10.1016/j.optlastec.2014.02.015.
[38]LUO Y L, LIN J, LIU J X, et al. A robust image encryption algorithm based on Chua’s circuit and compressive sensing[J]. Signal Processing, 2019, 161: 227-247. DOI:10.1016/j.sigpro.2019.03.022.
[39]杜鑫昌, 高瑜翔, 曹远杰,等. 基于混沌压缩感知和DNA编码的多图像加密算法[J]. 无线电工程, 2022, 52(3): 476-483.
[40]ZHU S Q, ZHU C X. A new image compression-encryption scheme based on compressive sensing and cyclic shift[J]. Multimedia Tools and Applications, 2019, 78(15): 20855-20875. DOI:10.1007/s11042-019-7405-y.
[41]GAN L. Block compressed sensing of natural images[C]// 2007 15th International Conference on Digital Signal Processing. Piscataway, NJ: IEEE, 2007: 403-406. DOI:10.1109/ICDSP.2007.4288604.
[42]ZHANG Y S, ZHOU J T, CHEN F, et al. A block compressive sensing based scalable encryption framework for protecting significant image regions[J]. International Journal of Bifurcation and Chaos, 2016, 26(11): 1650191. DOI:10.1142/S0218127416501911.
[43]ZHU L Y, SONG H S, ZHANG X, et al. A novel image encryption scheme based on nonuniform sampling in block compressive sensing[J]. IEEE Access, 2019, 7: 22161-22174. DOI:10.1109/ACCESS.2019.2897721.
[44]CHAI X L, FU X L, GAN Z H, et al. An efficient chaos-based image compression and encryption scheme using block compressive sensing and elementary cellular automata[J]. Neural Computing and Applications, 2020, 32(9): 4961-4988.
[45]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. DOI:10.1016/j.sigpro.2020.107629.
[46]CHEN G, LI D F, ZHANG J S. Iterative gradient projection algorithm for two-dimensional compressive sensing sparse image reconstruction[J]. Signal Processing, 2014, 104: 15-26. DOI:10.1016/j.sigpro.2014.03.039.
[47]EFTEKHARI A, BABAIE-ZADEH M, MOGHADDAM H A. Two-dimensional random projection[J]. Signal Processing, 2011, 91(7): 1589-1603. DOI:10.1016/j.sigpro.2011.01.002.
[48]ZHOU N R, LI H L, WANG D, et al. Image compression and encryption scheme based on 2D compressive sensing and fractional Mellin transform[J]. Optics Communications, 2015, 343: 10-21. DOI:10.1016/j.optcom.2014.12.084.
[49]ZHOU N R, PAN S M, CHENG S, et al. Image compression-encryption scheme based on hyper-chaotic system and 2D compressive sensing[J]. Optics & Laser Technology, 2016, 82: 121-133. DOI:10.1016/j.optlastec.2016.02.018.
[50]GAN Z H, BI J Q, DING W K, et al. Exploiting 2D compressed sensing and information entropy for secure color image compression and encryption[J]. Neural Computing and Applications, 2021, 33(19): 12845-12867. DOI:10.1007/s00521-021-05937-4.
[51]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. DOI:10.1016/j.ins.2020.10.007.
[52]ZHOU Y C, AGAIAN S. Image encryption using the image steganography concept and PLIP model[C]// Proceedings of 2011 International Conference on System Science and Engineering. Piscataway, NJ: IEEE, 2011: 699-703. DOI:10.1109/ICSSE.2011.5961993.
[53]BAO L, ZHOU Y C. Image encryption: Generating visually meaningful encrypted images[J]. Information Sciences, 2015, 324: 197-207. DOI:10.1016/j.ins.2015.06.049.
[54]KANSO A, GHEBLEH M. An algorithm for encryption of secret images into meaningful images[J]. Optics and Lasers in Engineering, 2017, 90: 196-208. DOI:10.1016/j.optlaseng.2016.10.009.
[55]WEN W Y, ZHANG Y S, FANG Y M, et al. Image salient regions encryption for generating visually meaningful ciphertext image[J]. Neural Computing and Applications, 2018, 29(3): 653-663. DOI:10.1007/s00521-016-2490-6.
[56]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. DOI:10.1016/j.sigpro.2016.11.016.
[57]WANG H, XIAO D, LI M, et al. A visually secure image encryption scheme based on parallel compressive sensing[J]. Signal Processing, 2019, 155(1): 218-232. DOI:10.1016/j.sigpro.2018.10.001.
[58]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. DOI:10.1016/j.optlaseng.2019.105837.
[59]JIANG D H, LIU L D, ZHU L Y, et al. Double-image visually meaningful encryption algorithm based on compressed sensing and FRFT embedding[J/OL]. Research Square: 1-23[2022-01-20]. https://doi.org/10.21203/rs.3.rs-291468/v1.
[60]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. DOI:10.1016/j.ins.2021.06.032.
[61]YE G D, PAN C, DONG Y X, et al. Image encryption and hiding algorithm based on compressive sensing and random numbers insertion[J]. Signal Processing, 2020, 172: 107563. DOI:10.1016/j.sigpro.2020.107563.
[62]WEN W Y, HONG Y K, FANG Y M, et al. A visually secure image encryption scheme based on semi-tensor product compressed sensing[J]. Signal Processing, 2020, 173: 107580. DOI:10.1016/j.sigpro.2020.107580.
[63]HUA Z Y, ZHANG K Y, LI Y M, et al. Visually secure image encryption using adaptive-thresholding sparsification and parallel compressive sensing[J]. Signal Processing, 2021, 183: 107998. DOI:10.1016/j.sigpro.2021.107998.
[64]WANG K S, WU X J, GAO T G. Double color images compression-encryption via compressive sensing[J]. Neural Computing and Applications, 2021, 33(19): 12755-12776. DOI:10.1007/s00521-021-05921-y.
[1] 邵慧婷, 杨启贵. 具有4个正Lyapunov指数的六维超混沌系统的复杂动力学研究[J]. 广西师范大学学报（自然科学版）, 2022, 40(5): 433-444.
[2] 李蓝航, 丘森辉, 王文仪, 肖丁维, 罗玉玲. 基于混沌映射的彩色图像多层交互加密算法[J]. 广西师范大学学报（自然科学版）, 2021, 39(6): 72-86.
[3] 阮文静, 杨启贵. 具有有限和无限孤立奇点的新四维超混沌系统的复杂动力学分析[J]. 广西师范大学学报（自然科学版）, 2021, 39(5): 173-181.
[4] 胡锦铭, 韦笃取. 不同阶次分数阶永磁同步电机的混合投影同步[J]. 广西师范大学学报（自然科学版）, 2021, 39(4): 1-8.
[5] 李松涛, 李群宏, 张文. 三自由度碰撞振动系统的余维二擦边分岔与混沌控制[J]. 广西师范大学学报（自然科学版）, 2021, 39(4): 79-92.
[6] 李蓝航, 丘森辉, 肖丁维, 黎亮佳, 欧阳雪, 罗玉玲. 基于DNA序列和动态索引扩散的图像加密算法[J]. 广西师范大学学报（自然科学版）, 2021, 39(3): 40-53.
[7] 吴雷, 阳志, 张磊, 白克钊. 分数阶混沌同步磁阻电机的滑模控制[J]. 广西师范大学学报（自然科学版）, 2021, 39(2): 62-70.
[8] 胡锦铭, 韦笃取. 分数阶永磁同步电机的广义同步研究[J]. 广西师范大学学报（自然科学版）, 2020, 38(6): 14-20.
[9] 周戎龙,罗玉玲,闭金杰,岑明灿,丘森辉,廖志贤. 图像并行加密算法在手持设备上的应用研究[J]. 广西师范大学学报（自然科学版）, 2019, 37(3): 60-70.
[10] 洪玲玲,杨启贵. 新四维超混沌系统的复杂动力学研究[J]. 广西师范大学学报（自然科学版）, 2019, 37(3): 96-105.
[11] 张立升, 张智勇, 马凯华, 李国放. 用改进的Lattice Boltzmann模型研究对流Cahn-Hilliard系统振荡[J]. 广西师范大学学报（自然科学版）, 2019, 37(2): 15-26.
[12] 吴雷, 阳丽, 李啟尚, 萧华鹏. 基于小增益定理的同步磁阻电机混沌控制[J]. 广西师范大学学报（自然科学版）, 2019, 37(2): 44-51.
[13] 吴雷,阳丽,郭鹏霄. Rucklidge系统的反馈线性化控制[J]. 广西师范大学学报（自然科学版）, 2017, 35(1): 21-27.
[14] 俞春强, 付睿, 陈艳, 雷芸, 张显全, 王晓云. 基于信息隐藏的图像感兴趣区域保护算法[J]. 广西师范大学学报（自然科学版）, 2016, 34(2): 67-73.
[15] 徐胜舟, 许向阳, 胡怀飞, 李波. 基于改进动态规划的MR图像左心室分割[J]. 广西师范大学学报（自然科学版）, 2014, 32(2): 35-41.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
[1] 张喜龙, 韩萌, 陈志强, 武红鑫, 李慕航. 面向复杂数据流的集成分类综述[J]. 广西师范大学学报（自然科学版）, 2022, 40(4): 1 -21 .
[2] 童凌晨, 李强, 岳鹏鹏. 基于CiteSpace的喀斯特土壤有机碳研究进展[J]. 广西师范大学学报（自然科学版）, 2022, 40(4): 22 -34 .
[3] 帖军, 隆娟娟, 郑禄, 牛悦, 宋衍霖. 基于SK-EfficientNet的番茄叶片病害识别模型[J]. 广西师范大学学报（自然科学版）, 2022, 40(4): 104 -114 .
[4] 翁烨, 邵德盛, 甘淑. 等式约束病态最小二乘的主成分Liu估计解法[J]. 广西师范大学学报（自然科学版）, 2022, 40(4): 115 -125 .
[5] 覃城阜, 莫芬梅. C3-和C4-临界连通图的结构[J]. 广西师范大学学报（自然科学版）, 2022, 40(4): 145 -153 .
[6] 贺青, 刘剑, 韦联福. 微弱电磁信号的物理极限检测:单光子探测器及其研究进展[J]. 广西师范大学学报（自然科学版）, 2022, 40(5): 1 -23 .
[7] 田芮谦, 宋树祥, 刘振宇, 岑明灿, 蒋品群, 蔡超波. 逐次逼近型模数转换器研究进展[J]. 广西师范大学学报（自然科学版）, 2022, 40(5): 24 -35 .
[8] 张师超, 李佳烨. 知识矩阵表示[J]. 广西师范大学学报（自然科学版）, 2022, 40(5): 36 -48 .
[9] 郝雅茹, 董力, 许可, 李先贤. 预训练语言模型的可解释性研究进展[J]. 广西师范大学学报（自然科学版）, 2022, 40(5): 59 -71 .
[10] 于梦竹, 唐振军. 基于手工特征的视频哈希研究综述[J]. 广西师范大学学报（自然科学版）, 2022, 40(5): 72 -89 .
版权所有 © 广西师范大学学报（自然科学版）编辑部
地址：广西桂林市三里店育才路15号　邮编：541004
电话：0773-5857325　E-mail: gxsdzkb@mailbox.gxnu.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发