IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v245y2026icp751-776.html

Integer reversible Racah transform and enhanced chaotic maps for 1D/2D signal reconstruction and secure image encryption

Author

Listed:
  • El-khanchouli, Karim
  • Joudar, Nour-Eddine
  • Sayyouri, Mhamed

Abstract

Traditional discrete orthogonal transforms, such as the Racah transform, often lead to reconstruction errors, limiting their application in digital signal processing tasks that require exact signal recovery. To overcome this limitation, this article introduces the Integer Reversible Racah Transform (IRT), a novel integer-based discrete transform specifically designed for lossless signal processing on resource-constrained platforms. Additionally, considering the significant role of chaotic systems in encryption, we propose an enhanced chaotic scheme that improves upon the logistic map-based chaotic model. The classical logistic map has cryptographic limitations due to its single control parameter, narrow sensitivity range to initial conditions, and the potential periodicity of generated sequences. To address these issues, the proposed chaotic map in this study offers a broader chaotic interval and an expanded control parameter space, ensuring more secure key generation for encryption systems. By combining the IRT with our proposed chaotic system, we develop an efficient encryption and decryption scheme. Experimental results demonstrate that this scheme provides substantial resistance against various attacks while maintaining nearly intact quality in decrypted images. This highlights not only the effectiveness of the encryption scheme but also its enhanced security and robustness. Compared to existing methods, our scheme stands out for its exceptional reliability and robustness, significantly contributing to the secure protection of images.

Suggested Citation

  • El-khanchouli, Karim & Joudar, Nour-Eddine & Sayyouri, Mhamed, 2026. "Integer reversible Racah transform and enhanced chaotic maps for 1D/2D signal reconstruction and secure image encryption," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 245(C), pages 751-776.
  • Handle: RePEc:eee:matcom:v:245:y:2026:i:c:p:751-776
    DOI: 10.1016/j.matcom.2025.06.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475425002393
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.matcom.2025.06.008?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Achraf Daoui & Haokun Mao & Mohamed Yamni & Qiong Li & Osama Alfarraj & Ahmed A. Abd El-Latif, 2023. "Novel Integer Shmaliy Transform and New Multiparametric Piecewise Linear Chaotic Map for Joint Lossless Compression and Encryption of Medical Images in IoMTs," Mathematics, MDPI, vol. 11(16), pages 1-28, August.
    2. 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).
    3. Zhou, Shuang & Zhao, Zhipeng & Wang, Xingyuan, 2022. "Novel chaotic colour image cryptosystem with deep learning," Chaos, Solitons & Fractals, Elsevier, vol. 161(C).
    4. Ernesto Moya-Albor & Andrés Romero-Arellano & Jorge Brieva & Sandra L. Gomez-Coronel, 2023. "Color Image Encryption Algorithm Based on a Chaotic Model Using the Modular Discrete Derivative and Langton’s Ant," Mathematics, MDPI, vol. 11(10), pages 1-35, May.
    5. Liu, Hongjun & Kadir, Abdurahman & Xu, Chengbo, 2020. "Cryptanalysis and constructing S-Box based on chaotic map and backtracking," Applied Mathematics and Computation, Elsevier, vol. 376(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    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. Manuel Alejandro Cardona-López & Juan Carlos Chimal-Eguía & Víctor Manuel Silva-García & Rolando Flores-Carapia, 2024. "Statistical Analysis of the Negative–Positive Transformation in Image Encryption," Mathematics, MDPI, vol. 12(6), pages 1-23, March.
    4. Víctor Manuel Silva-García & Rolando Flores-Carapia & Manuel Alejandro Cardona-López & Miguel Gabriel Villarreal-Cervantes, 2023. "Generation of Boxes and Permutations Using a Bijective Function and the Lorenz Equations: An Application to Color Image Encryption," Mathematics, MDPI, vol. 11(3), pages 1-25, January.
    5. Yang, Yichen & Ding, Zhenglong & Jiang, Feng, 2026. "A synergistic pseudo-random number generator based on training-free multi-scale autoregressive Transformer and 3D-Lü chaos with application to image encryption," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 694(C).
    6. Li, Xuejun & Mou, Jun & Banerjee, Santo & Wang, Zhisen & Cao, Yinghong, 2022. "Design and DSP implementation of a fractional-order detuned laser hyperchaotic circuit with applications in image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    7. Wang, Meng-meng & Song, Xin-guang & Zhou, Nan-run & Liu, Si-hang, 2024. "Novel 1-D enhanced Log-logistic chaotic map and asymmetric generalized Gaussian apertured FrFT for image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 187(C).
    8. Ustun, Deniz & Sahinkaya, Serap, 2025. "Design of secure S-Boxes via novel 2D-Zettle chaotic map and ABC algorithm for robust image encryption," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 235(C), pages 175-204.
    9. Huang, Yuanyuan & Huang, Huijun & Huang, Yunchang & Wang, Yinhe & Yu, Fei & Yu, Beier & Liu, Chenghao, 2024. "Asymptotic shape synchronization in three-dimensional chaotic systems and its application in color image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 184(C).
    10. Contreras-Reyes, Javier E. & Kharazmi, Omid, 2023. "Belief Fisher–Shannon information plane: Properties, extensions, and applications to time series analysis," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    11. Tuli, Rohan & Soneji, Hitesh Narayan & Churi, Prathamesh, 2022. "PixAdapt: A novel approach to adaptive image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    12. Wang, Mingxu & Fu, Xianping & Teng, Lin & Yan, Xiaopeng & Xia, Zhiqiu & Liu, Pengbo, 2024. "A new 2D-HELS hyperchaotic map and its application on image encryption using RNA operation and dynamic confusion," Chaos, Solitons & Fractals, Elsevier, vol. 183(C).
    13. Hemalatha Mahalingam & Sivaraman Rethinam & Siva Janakiraman & Amirtharajan Rengarajan, 2023. "Non-Identical Inverter Rings as an Entropy Source: NIST-90B-Verified TRNG Architecture on FPGAs for IoT Device Integrity," Mathematics, MDPI, vol. 11(4), pages 1-18, February.
    14. Zhang, Jie & Yang, Liu & Zuo, Jiangang & Wei, Xiaodong & Cheng, Nana, 2025. "Design and application of spatial multi-structure hidden attractors in memristor-coupled heterogeneous neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 199(P1).
    15. Lai, Qiang & Chen, Yidan, 2025. "Effect of electromagnetic radiation on double-loop neural networks and its application to image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 194(C).
    16. Lai, Qiang & Chen, Yidan, 2024. "Design and encryption application of multi-scroll chain-loop memristive neural networks with initial-boosting coexisting attractors," Chaos, Solitons & Fractals, Elsevier, vol. 187(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:matcom:v:245:y:2026:i:c:p:751-776. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.