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Exploiting one-dimensional exponential Chebyshev chaotic map and matching embedding for visually meaningful image encryption

Author

Listed:
  • Long, Guoqiang
  • Chai, Xiuli
  • Gan, Zhihua
  • Jiang, Donghua
  • He, Xin
  • Sun, Mengge

Abstract

As a result of their prominent initial value sensitivity, pseudo-randomness, and unpredictability nature, chaotic maps are widely used in multimedia security. However, the majority of chaotic maps now in use have issues like narrow parameter scope, poor traversability, and insufficient chaotic performance. To address these issues, a one-dimensional exponential Chebyshev (1-DEC) chaotic map is constructed in this paper which performs satisfactorily. Through the use of a bifurcation diagram, Lyapunov exponents, sample entropy, and permutation entropy, the chaotic dynamics of the 1-DEC are thoroughly examined. Subsequently, a visually meaningful image encryption (VMIE) scheme combined with matching embedding based on 1-DEC was presented. Specifically, a cyclic shift confusion method across rows and columns (CSCARC) is proposed to eliminate the correlation of adjacent pixels. Then, an adaptive embedding method based on image energy and dynamic matching (AEIEDM) is proposed to fuse the privacy information into carrier images. In addition, secret image share and authentication information is introduced in this scheme, which improves disaster-tolerance performance and implements the distributed management and storage of data. Subscribers have the capabilities of authentication, management, and data integrity checking. Ultimately, the simulation results and analysis demonstrate that the proposed scheme possesses satisfactory performance in terms of security and robustness, and the visual quality of the steganographic images was above 50.9 dB.

Suggested Citation

  • Long, Guoqiang & Chai, Xiuli & Gan, Zhihua & Jiang, Donghua & He, Xin & Sun, Mengge, 2023. "Exploiting one-dimensional exponential Chebyshev chaotic map and matching embedding for visually meaningful image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    • Handle: RePEc:eee:chsofr:v:176:y:2023:i:c:s0960077923010123
    DOI: 10.1016/j.chaos.2023.114111
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    References listed on IDEAS

    as
    1. Zhou, Shuang & Zhao, Zhipeng & Wang, Xingyuan, 2022. "Novel chaotic colour image cryptosystem with deep learning," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    2. Liu, Xilin & Tong, Xiaojun & Zhang, Miao & Wang, Zhu, 2023. "A highly secure image encryption algorithm based on conservative hyperchaotic system and dynamic biogenetic gene algorithms," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    3. Zhou, Shuang & Qiu, Yuyu & Qi, Guoyuan & Zhang, Yingqian, 2023. "A new conservative chaotic system and its application in image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    4. Huang, Shufeng & Jiang, Donghua & Wang, Qianxue & Guo, Mingwei & Huang, Linqing & Li, Weijun & Cai, Shuting, 2022. "High-quality visually secure image cryptosystem using improved Chebyshev map and 2D compressive sensing model," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).
    5. Wang, Xingyuan & Du, Xiaohui, 2022. "Pixel-level and bit-level image encryption method based on Logistic-Chebyshev dynamic coupled map lattices," Chaos, Solitons & Fractals, Elsevier, vol. 155(C).
    6. Wang, Xingyuan & Liu, Cheng & Jiang, Donghua, 2022. "Visually meaningful image encryption scheme based on new-designed chaotic map and random scrambling diffusion strategy," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    7. Lai, Qiang & Hu, Genwen & Erkan, Uǧur & Toktas, Abdurrahim, 2023. "High-efficiency medical image encryption method based on 2D Logistic-Gaussian hyperchaotic map," Applied Mathematics and Computation, Elsevier, vol. 442(C).
    8. Lai, Qiang & Chen, Zhijie, 2023. "Grid-scroll memristive chaotic system with application to image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
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