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Cited by 168Chaos-based Image/Signal EncryptionComputer Science and EngineeringAdvanced Steganography and Watermarking Techniques

A Parallel Color Image Encryption Algorithm Based on a 2-D Logistic-Rulkov Neuron Map

Gao, Suo, Zhang, Zheyi, Iu, Herbert Ho-Ching, Ding, Siqi, Mou, Jun, Erkan, Uğur, Toktas, Abdurrahim, Li, Qi +2 more·2025·IEEE Internet of Things Journal
DOI 10.1109/jiot.2025.3540097
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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%.

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Crossref · 10.1109/jiot.2025.3540097

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  • FPGA-based data possess strong characteristics in terms of complexity measures, comparable to systems based on chaotic maps reported in the literature.

    “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

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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
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2025-06-01
Volume
12
Issue
11
Pages
18115-18124
ISSN
  • 2327-4662 (ISSN-L)
  • 2372-2541
DOI
10.1109/jiot.2025.3540097
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

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