Cited by 81Chaos-based Image/Signal EncryptionCellular Automata and ApplicationsComputer Science and Engineering

Image encryption method based on chaotic fuzzy cellular neural networks

Ratnavelu, K., Kalpana, M., Balasubramaniam, P., Wong, K., Raveendran, P.·2017·Signal Processing

Integrity signals

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Crossref · 10.1016/j.sigpro.2017.05.002

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2 of 2 references verified0 citation errors · 0 not checkable

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  • K. Ratnavelu and M. Kalpana [127] introduced a fuzzy cellular neural network (FCNN).

    “A construct called a fuzzy cellular neural network (FCNN) was introduced by K. Ratnavelu and M. Kalpana [127].”

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Authors
Ratnavelu, K., Kalpana, M., Balasubramaniam, P., Wong, K., & Raveendran, P.
Affiliations
  • University of Malaya (MY)
  • Gandhigram Rural Institute (IN)
  • Monash University Malaysia (MY)
Corresponding author
Wong, K.
Venue
Signal Processing
Publisher
Elsevier BV
Published
2017-11
Volume
140
Pages
87-96
ISSN
0165-1684 (ISSN-L)
OpenAlex
W2613745606
Cited by
81
References
2
Citations by year
2026: 6 · 2025: 6 · 2024: 8 · 2023: 5 · 2022: 11 · 2021: 14 · 2020: 12 · 2019: 10 · 2018: 8 · 2017: 1
FWCI
3.52
Type
journal article
Language
en
Open access
closed
Locations
  • Signal Processing (publishedVersion)
  • The University of Malaya Research Repository (University of Malaya) (submittedVersion)
Funders
MoHE (FP051-2016)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Chaos-based Image/Signal Encryption
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Cellular Automata and Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Computer Science and Engineering
Keywords
image encryption, fuzzy cellular neural networks, chaotic signals, chaotic maps, key sensitivity, brute-force attack resistance, chosen-plaintext attack resistance

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