The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
A Parallel Color Image Encryption Algorithm Based on a 2-D Logistic-Rulkov Neuron Map
Abstract
Images are widely used in social networks, necessitating efficient and secure transmission, especially in bandwidth-constrained environments. This article aims to develop a color image encryption algorithm that enhances security while optimizing computational efficiency. A novel parallel color image encryption algorithm based on the 2-D logistic-Rulkov neuron map (2D-LRNM) is proposed. In this approach, the three channels of the color image are first separated. Cross-channel information interaction is introduced to form three new channels, which are then processed in parallel. During the encryption process of each channel, a block-wise parallel encryption mechanism is applied, ensuring simultaneous encryption of each block. This block-wise strategy effectively leverages parallel computing resources and balances the task load. To meet the demand for a large number of keystreams during encryption, the 2D-LRNM is introduced. It combines the simplicity and chaotic properties of the Logistic map with the multitimescale dynamics and neurodynamic behaviors of the Rulkov map. By overcoming the dimensional limitations inherent in the single Logistic map, this approach extends the system to a 2-D framework, significantly increasing the complexity of chaotic behavior and improving its unpredictability. Experimental results demonstrate that the proposed encryption algorithm achieves high security and reduces computation time by approximately 83.3%.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“These findings indicate that FPGA-based data possess strong characteristics in terms of complexity measures, comparable to systems based on chaotic maps reported in the literature (Gao et al. 2025b(Gao et al. , 2025c)).”
From A novel deep learning-based statistical randomness evaluation test methodology for cryptographic applications (2025) · cites this paper as Gao et al. 2025c
Citations
Cites
Regularization of Synchronized Chaotic Bursts
Rulkov, N. F. (2001) · Physical Review Letters · doi:10.1103/physrevlett.86.183 · Cited by 459
Kong, X., Yu, F., Yao, W., Cai, S., Zhang, J., & Lin, H. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.12.008 · Cited by 292
Color Image Encryption Through Chaos and KAA Map
Alexan, W., Elkandoz, M. T., Mashaly, M. E., Azab, E., & Aboshousha, A. (2023) · IEEE Access · doi:10.1109/access.2023.3242311 · Cited by 248
Feng, W., Zhang, J., Chen, Y., Qin, Z., Zhang, Y., Ahmad, M., & Woźniak, M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123190 · Cited by 210
Feng, W., Wang, Q., Liu, H., Ren, Y., Zhang, J., Zhang, S., Qian, K., & Wen, H. (2023) · Fractal and Fractional · doi:10.3390/fractalfract7120887 · Cited by 174
Meng, F.-Q., & Wu, G. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124413 · Cited by 144
Toktas, F., Erkan, U., & Yetgín, Z. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.123583 · Cited by 144
Self-citationColor multiple image encryption scheme based on 3D-chaotic maps
Malik, D. S., & Shah, T. (2020) · Mathematics and Computers in Simulation · doi:10.1016/j.matcom.2020.07.007 · Cited by 132
Secure medical image encryption scheme for Healthcare IoT using novel hyperchaotic map and DNA cubes
Lai, Q., & Hua, H. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125854 · Cited by 127
EFR-CSTP: Encryption for face recognition based on the chaos and semi-tensor product theory
Gao, S., Wu, R., Wang, X., Liu, J., Li, Q., & Tang, X. (2022) · Information Sciences · doi:10.1016/j.ins.2022.11.121 · Cited by 116
Self-citation
Cited by
Sorted by citations
Gao, S., Zhang, Z., Li, Q., Ding, S., Iu, H. H., Cao, Y., Xu, X., Wang, C., & Mou, J. (2025) · IEEE Transactions on Dependable and Secure Computing · doi:10.1109/tdsc.2025.3603570 · Cited by 192
Self-citationChaos-based video encryption techniques: A review
Gao, S., Wu, R., Iu, H. H., Erkan, U., Cao, Y., Li, Q., Toktaş, A., & Mou, J. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100816 · Cited by 121
Self-citationGao, S., Ding, S., Ho-Ching Iu, H., Erkan, U., Toktas, A., Simsek, C., Wu, R., Xu, X., Cao, Y., & Mou, J. (2025) · Chaos: An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0270220 · Cited by 100
Self-citationHe, T., Yu, F., 林, Y. 越. L., He, S., Yao, W., Cai, S., & Jin, J. (2025) · Chinese Physics B · doi:10.1088/1674-1056/adfeff · Cited by 54
He, S., Yu, F., Guo, R., Zheng, M., Tang, T., Jin, J., & Wang, C. (2025) · Fractal and Fractional · doi:10.3390/fractalfract9090561 · Cited by 49
Self-citation
Metadata
- Authors
- Gao, S., Zhang, Z., Iu, H. H.-C., Ding, S., Mou, J., Erkan, U., Toktas, A., Li, Q., Wang, C., & Cao, Y.
- Affiliations
- Dalian Polytechnic University (CN)
- The University of Western Australia (AU)
- Dalian University of Technology (CN)
- Karamanoğlu Mehmetbey University (TR)
- Department of Artificial Intelligence and Data Engineering, Engineering Faculty, Ankara University, Ankara, Türkiye
- Qilu University of Technology (CN)
- ORCID
- Venue
- IEEE Internet of Things Journal
- Published
- 2025-06-01
- Volume
- 12
- Issue
- 11
- Pages
- 18115-18124
- ISSN
- 2327-4662 (ISSN-L)
- 2372-2541
- OpenAlex
- W4407304478
- Cited by
- 168
- References
- 40
- Citations by year
- 2026: 65 · 2025: 103
- FWCI
- 50.84
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Internet of Things Journal (publishedVersion)
- UWA Profiles and Research Repository (UWA) (submittedVersion)
- Funders
- Liaoning Provincial Science and Technology Plan Joint Project (2024-MSLH-033)
- Technological Innovation Projects in the Field of Artificial Intelligence in Liaoning Province (2023JH26/10300011)
- 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 › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Keywords
- color image encryption, chaotic maps, parallel encryption, keystream generation, computational efficiency, secure image transmission
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
