CoChat
Sign Up
Cited by 25Chaos-based Image/Signal EncryptionComputer Science and EngineeringChaos control and synchronization

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

A LE-controlled 4D non-degenerate hyperchaotic system and STP-CS model based efficient image encryption algorithm

Long, Guoqiang, Verma, Vivek, Jiang, Donghua, Yang, Yuguang, Ahmad, Musheer·2025·Physica Scripta
DOI 10.1088/1402-4896/ad9d8b
Open this paper in CoChat — free account

Abstract

Restricted by the environment and hardware equipment resources, existing chaotic systems have shortcomings such as low complexity, low randomness, and chaotic degradation phenomena, which in turn cause the security risks of chaotic image encryption algorithms. To overcome these issues, this paper proposes a method for the construction of a LE-controlled four-dimensional (4D) non-degenerate discrete chaotic system. Numerical analysis has demonstrated that the developed system possess high complexity and unpredictability. Based on the developed chaotic system, an image compression encryption algorithm is proposed. Wherein, semi-tensor product compressed sensing is applied to allow data compression sampling in different dimensions resulting in reducing the data transfer load and storage cost. Subsequently, the positions and values of the image pixels are secretly altered during the algorithmic encryption process using two-dimensional cat confusion and finite field diffusion. The simulation results show that the proposed encryption algorithm effective enough to offer great encryption quality. The performance comparison analysis indicates that the proposed encryption algorithm has good furnishes better security, compression, as well as resistance to diverse data attacks.

Overview

Integrity signals

Registry

Crossref · 10.1088/1402-4896/ad9d8b

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Chaos-based encryption methods introduce a high degree of non-linearity and complexity, which is crucial for thwarting cryptographic attacks.

    “Utilizing the inherent unpredictability and sensitivity to initial conditions characteristic of chaotic systems, chaos-based encryption methods introduce a high degree of non-linearity and complexity12–14. This is crucial for thwarting a variety of cryptographic attacks, enhancing the security of multimedia files.”

    From A chaos-based augmented image encryption scheme for satellite images using Fredkin logic (2025) · cites this paper as [14]

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 58

  1. Rong, 2022, Nonlinear Dyn., 110, 2831, 10.1007/s11071-022-07736-5 · DOI
  2. Ramasamy, 2020, Wirel. Pers. Comm., 112, 1355, 10.1007/s11277-020-07106-7 · DOI
  3. Jiang, 2021, Signal Process., 188, 108220, 10.1016/j.sigpro.2021.108220 · DOI
  4. Patel, 2024, Multimedia Tools App., 83, 40733, 10.1007/s11042-023-17271-z · DOI
  5. Wen, 2024, Expert Sys. App., 237, 10.1016/j.eswa.2023.121514 · DOI
  6. Li, 2024, Multimedia Tools App., 83, 5351, 10.1007/s11042-023-15550-3 · DOI · Paper page
  7. Darani, 2024, Chaos Sol. & Frac., 179, 114396, 10.1016/j.chaos.2023.114396 · DOI
  8. Alkhonaini, 2024, Sci. Rep., 14, 10.1038/s41598-024-64741-x · DOI
  9. Brahim, 2024, J. Opt., 53, 1170, 10.1007/s12596-023-01232-8 · DOI
  10. Vijayakumar, 2024, Ain Shams Eng. Jour., 15, 10.1016/j.asej.2023.102620 · DOI
  11. Zhang, 2023, Phys. Scr., 98, 095217, 10.1088/1402-4896/aceb9b · DOI
  12. Fan, 2024, Phys. Scr., 99, 055216, 10.1088/1402-4896/ad3698 · DOI · Paper page
  13. Xiangjun, 2018, Signal Process., 148, 272, 10.1016/j.sigpro.2018.02.028 · DOI · Paper page
  14. Wang, 2016, Nonlinear Dyn., 84, 1417, 10.1007/s11071-015-2579-y · DOI
  15. Rani, 2022, Nonlinear Dyn., 111, 2869, 10.1007/s11071-022-07958-7 · DOI
  16. Su, 2022, Nonlinear Dyn., 111, 2779, 10.1007/s11071-022-07930-5 · DOI
  17. Zhong, 2022, Mathematics, 10, 2583, 10.3390/math10152583 · DOI
  18. Xu, 2020, Opt. Lasers Eng., 134, 106178, 10.1016/j.optlaseng.2020.106178 · DOI
  19. Liu, 2022, Chaos Sol. & Frac., 154, 111693, 10.1016/j.chaos.2021.111693 · DOI
  20. Verma, 2024, Nonlinear Dyn., 112, 10583, 10.1007/s11071-024-09620-w · DOI
  21. Kumar, 2024, IETE Jou. of Res., 25
  22. An, 2024, Expert Sys. App., 243, 10.1016/j.eswa.2023.122899 · DOI
  23. Wang, 2024, Chaos Sol. & Frac., 155, 111629, 10.1016/j.chaos.2021.111629 · DOI
  24. Sicong, 2022, J. Elec. Info. Tec., 44, 1754
  25. Chen, 2002, Phys. Lett. A, 299, 353, 10.1016/S0375-9601(02)00522-4 · DOI
  26. Gu, 2022, Nonlinear Dyn., 111, 7807, 10.1007/s11071-022-08201-z · DOI
  27. Liu, 2022, Nonlinear Dyn.., 110, 1897, 10.1007/s11071-022-07702-1 · DOI
  28. Gang, 2022, Phys. A: Stat. Mec. App, 591, 10.1016/j.physa.2021.126793 · DOI
  29. Li, 2024, Chin. Phy. B, 33, 030505, 10.1088/1674-1056/acf281 · DOI
  30. Cao, 2024, Phys. Scr., 99, 095023, 10.1088/1402-4896/ad6f79 · DOI
  31. Lu, 2023, Journal of King Saud, Univ. Comp. Info. Sci, 35, 37
  32. Huang, 2020, Mathematical Prob. Eng., 1, 3965281
  33. Gao, 2024, Applied Math. Mod., 134, 520, 10.1016/j.apm.2024.06.016 · DOI
  34. Gao, 2024, Chaos Sol. & Frac., 183, 114958, 10.1016/j.chaos.2024.114958 · DOI
  35. Gao, 2024, IEEE Int. of Thi. Jou., 11, 21815
  36. Rani, 2022, Multimedia Tools App., 82, 19391
  37. Taghouti, 2020, Computing Comm. Net., 197, 10.1016/B978-0-12-820488-7.00023-2 · DOI
  38. Kim, 2012, IEEE Trans. Ima. Proce., 21, 3102, 10.1109/TIP.2012.2188807 · DOI
  39. Liu, 2013, Optik, 124, 6590, 10.1016/j.ijleo.2013.05.092 · DOI
  40. Yao, 2019, Optik Las. Tec., 120, 10.1016/j.optlastec.2019.105703 · DOI
  41. Zhou, 2014, Optics Las, Tec., 62, 152
  42. Lohit, 2018, IEEE Trans. Comp. Imag., 4, 326
  43. Zhou, 2020, IEEE Trans. Multi., 23, 2627, 10.1109/TMM.2020.3014561 · DOI
  44. Hua, 2021, Signal Proc., 183, 107998, 10.1016/j.sigpro.2021.107998 · DOI
  45. Fang, 2021, IEEE Trans. Sig. Proc., 62, 196, 10.1109/TSP.2013.2284762 · DOI
  46. Liu, 2016, Signal Processing: Image Comm., 45, 41, 10.1016/j.image.2016.04.002 · DOI
  47. Zhang, 2016, Neurocomputing, 205, 472, 10.1016/j.neucom.2016.04.053 · DOI
  48. Xiuli, 2018, Signal Proc., 148, 124, 10.1016/j.sigpro.2018.02.007 · DOI · Paper page
  49. Liu, 2021, Applied Math. Mod., 130, 16, 10.1016/j.apm.2024.02.009 · DOI
  50. Long, 2023, Chaos Sol. & Frac., 176, 114111, 10.1016/j.chaos.2023.114111 · DOI
  51. Ming, 2022, Nonlinear Dyn., 111, 847, 10.1007/s11071-022-07838-0 · DOI
  52. Zhang, 2022, IEEE Trans on Ind. Info., 18, 8434, 10.1109/TII.2022.3151984 · DOI
  53. Zhao, 2022, Nonlinear Dyn., 110, 821, 10.1007/s11071-022-07641-x · DOI
  54. Wu, 2021, Cogn. Neuro, 15, 157, 10.1007/s11571-020-09583-9 · DOI
  55. Wang, 2021, Signal Process.: Image Comm., 95, 10.1016/j.image.2021.116246 · DOI
  56. Toktas, 2021, Nonlinear Dyn., 105, 1885, 10.1007/s11071-021-06675-x · DOI
  57. Zourmba, 2024, Mathematical and Comp in Simu., 227, 58, 10.1016/j.matcom.2024.07.035 · DOI
  58. Mfungo, 2023, Applied Sci., 13

Cited by

Sorted by citations

Metadata

Authors
Long, G., Verma, V., Jiang, D., Yang, Y., & Ahmad, M.
Affiliations
  • Southeast University (CN)
  • Indian Institute of Technology Dhanbad (IN)
  • Sun Yat-sen University (CN)
  • Jamia Millia Islamia (IN)
ORCID
Venue
Physica Scripta
Publisher
IOP Publishing
Published
2025-01-17
Volume
100
Issue
2
Pages
025228
ISSN
  • 0031-8949 (ISSN-L)
  • 1402-4896
DOI
10.1088/1402-4896/ad9d8b
OpenAlex
W4405269676
Cited by
25
References
3
Citations by year
2026: 8 · 2025: 17
FWCI
3.51
Type
journal article
Language
en
Open access
closed
Licence
Locations
Physica Scripta (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Chaos-based Image/Signal Encryption
  • Physical Sciences › Computer Science › Artificial Intelligence › Computer Science and Engineering
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Chaos control and synchronization
Keywords
chaotic image encryption, semi-tensor product, compressed sensing, pixel scrambling, attack resistance

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review