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一种基于光混沌和图像预处理机制的加密算法

周雪芳 孙乐 陈伟浩 郑宁

周雪芳, 孙乐, 陈伟浩, 郑宁. 一种基于光混沌和图像预处理机制的加密算法[J]. 电子与信息学报, 2023, 45(12): 4519-4529. doi: 10.11999/JEIT221332
引用本文: 周雪芳, 孙乐, 陈伟浩, 郑宁. 一种基于光混沌和图像预处理机制的加密算法[J]. 电子与信息学报, 2023, 45(12): 4519-4529. doi: 10.11999/JEIT221332
ZHOU Xuefang, SUN Le, CHEN Weihao, ZHENG Ning. An Encryption Algorithm Based on Optical Chaos and Image Quotient and Residue Preprocessing[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4519-4529. doi: 10.11999/JEIT221332
Citation: ZHOU Xuefang, SUN Le, CHEN Weihao, ZHENG Ning. An Encryption Algorithm Based on Optical Chaos and Image Quotient and Residue Preprocessing[J]. Journal of Electronics & Information Technology, 2023, 45(12): 4519-4529. doi: 10.11999/JEIT221332

一种基于光混沌和图像预处理机制的加密算法

doi: 10.11999/JEIT221332
基金项目: 国家自然科学基金 (61705055),浙江省重点研发计划 (2019C01G1121168),浙江省数据存储传输及应用技术研究重点实验室基金
详细信息
    作者简介:

    周雪芳:女,副教授,研究方向为光混沌图像加密、光混沌通信和光纤激光器技术

    郑宁:男,教授,研究方向为网络空间安全技术、图像加密算法和人工智能算法等

    通讯作者:

    郑宁 nzheng@hdu.edu.cn

  • 中图分类号: TN911.73; TP309.7; TN918.4

An Encryption Algorithm Based on Optical Chaos and Image Quotient and Residue Preprocessing

Funds: The National Natural Science Foundation of China (61705055), The Key Research and Development Program of Zhejiang Province (2019C01G1121168), The Key Laboratory Foundation of Data Storage and Transmission Technology of Zhejiang Province
  • 摘要: 随着现代科学技术的发展,人们对图像信息传输的安全性要求越来越高,以混沌理论为基础的图像加密方案更加受到重视。该文提出一种新型的光混沌图像加密传输系统以及图像“自加密”算法,该系统的主激光器(ML)经全光反馈后分别注入到3个半导体激光器(SLs)中,从而产生3个同步的混沌序列。在图像加密之前,先对明文图像进行预处理,得到两幅图像,一幅是明文图像取商的图像,另一幅是明文图像取余的图像。利用发送端的混沌序列对预处理的两幅图像进行多次加密、隐写以及扩散等操作,得到密文图像。实验结果表明:该文的密文图像像素值分布均匀,各像素间的相关性被打破,NPCR和UACI均接近理想值。该图像预处理的方法能够有效地使图像像素值更为集中、分布更加均匀,同时结合光混沌对图像进行加密,大大地提高传输图像的安全性。
  • 图  1  加密系统模型

    图  2  图像的预处理

    图  3  加密过程中产生的图像

    图  4  SL1和SL2, SL3的互相关系数

    图  5  明文图像、密文图像与解密图像

    图  6  明文与密文图像直方图

    7  明文与密文图像的像素值分布图

    图  8  密钥敏感性分析

    图  9  裁剪攻击分析

    表  1  激光器的参数

    参数数值参数数值
    $ \partial $3$ {C} $3×108
    $ {{K}_{f}} $30 ns$ {g} $1.2×10–5
    $ {\tau _{f}} $1.2 ns$ {{N}_0} $1.25×108
    $ {{K}_{{inj}}} $50$ {s} $5×10–7
    ${{e} }$1.6×10–19$ \gamma $496
    $ {\gamma _{e}} $0.65
    下载: 导出CSV

    表  2  直方图检测

    图像$ \chi _{{\text{test}}}^2 $结果
    明文Lena30665.7031不通过
    密文Lena243.7188通过
    明文Baboo42256.0859不通过
    密文Baboo232.8672通过
    明文Boat100313.132不通过
    密文Boat226.4297通过
    明文Camera109020.992不通过
    密文Camera247.4375通过
    下载: 导出CSV

    表  3  相关性检测

    算法水平垂直对角线反对角线
    明文Lena0.96990.93990.91630.9378
    密文Lena–0.0030–0.00280.00690.0211
    文献[18]0.000570.0028–0.0014
    文献[19]0.00170.000920.0011
    文献[20]0.0052–0.00011–0.0022
    文献[21]0.000430.0048–0.0040
    文献[22]0.000120.000110.00178
    文献[23]–0.00150.00180.0018
    下载: 导出CSV

    表  4  不同图像的NPCR和UACI

    算法NPCRUACI
    本文(Lena)99.629233.5504
    文献[24]99.514432.7495
    文献[25]99.611433.5499
    文献[26]99.610133.4583
    文献[27]99.614033.5463
    文献[28]99.6465233.46289
    下载: 导出CSV

    表  5  信息熵

    算法明文图像密文图像
    本文Lena7.56837.9976
    文献[29]7.38147.9564
    文献[30]7.44257.9975
    文献[31]7.445577.99935
    文献[32]7.4450777.99933
    文献[33]7.58277.9995
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-10-24
  • 修回日期:  2023-02-20
  • 网络出版日期:  2023-03-14
  • 刊出日期:  2023-12-26

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