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Cited by 44Neural Networks Stability and SynchronizationChaos-based Image/Signal EncryptionChaos control and synchronization

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Finite-Time Projective Synchronization of Memristor-Based BAM Neural Networks and Applications in Image Encryption

Open access
Wang, Weiping, Wang, Xiao, Luo, Xiong, Yuan, Manman·2018·IEEE Access
DOI 10.1109/access.2018.2872745
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Abstract

Inspired by security applications in the image transmission, this paper focuses on the usage of chaotic properties of memristor-based bidirectional associate memory neural networks (MBAMNNs) for image encryption against illegal attack. A class of memristor-based bidirectional associate memory neural networks with delays and stochastic perturbations is formulated and analyzed. Based on drive-response concept, Itô's differential formula and inequality technique, some sufficient criteria are obtained to guarantee the finite-time projective synchronization. In order to realize the image encryption, we propose a chaotic color image encryption algorithm based on MBAMNNs. Illustrative examples are provided to verify the developed finite-time projective synchronization results. And we also show the great chaotic properties of the models proposed in this paper. Analysis of the encryption effect demonstrated the security of the proposed image encryption algorithm, and the potential applications of our models in secure image transmission are analyzed.

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  • The proposed study's outcome is somewhat better than the authors' earlier implementation study, in which the image was protected using Chaotic Map, Deep Neural Network and Elliptical Curve Cryptography.

    “This outcome is also somewhat better version of our earlier implementation study, in which the image was protected using Chaotic Map [47], Deep Neural Network [52], and Elliptical Curve Cryptography [53].”

    From A Faster and Robust Artificial Neural Network Based Image Encryption Technique With Improved SSIM (2024) · cites this paper as [53]

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Metadata

Authors
Wang, W., Wang, X., Luo, X., & Yuan, M.
Affiliations
  • China Building Materials Academy (CN)
  • University of Science and Technology Beijing (CN)
ORCID
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2018
Volume
6
Pages
56457-56476
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2018.2872745
OpenAlex
W2893589831
Cited by
44
References
1
Citations by year
2026: 5 · 2025: 6 · 2024: 6 · 2023: 9 · 2022: 9 · 2021: 2 · 2020: 4 · 2019: 3
FWCI
1.39
Type
journal article
Language
en
Open access
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/OAPA.html (vor)
Locations
  • IEEE Access (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • National Key Research and Development Program of China (2017YFB0702300)
  • China Scholarship Council
  • National Natural Science Foundation of China (61603032, 61174103)
  • Fundamental Research Funds for the Central Universities (06500025)
  • National Key Technologies R&D Program of China (2015BAK38B01)
  • University of Science and Technology Beijing (TW201705)
Topics
  • Physical Sciences › Engineering › Control and Systems Engineering › Neural Networks Stability and Synchronization
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Chaos-based Image/Signal Encryption
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Chaos control and synchronization
Keywords
memristor-based neural networks, bidirectional associative memory neural networks, image encryption, time delay, random perturbations, master-slave synchronization, secure image transmission

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