Discrete memristive neuron model and its interspike interval-encoded application in image encryption
Integrity signals
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Cites
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Claims the paper makes
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
The encoded sequences of [131] exhibit higher complexity and better biological interpretability than the original burst sequences.
“The encoded sequences exhibit higher complexity and better biological interpretability than the original burst sequences.”
From Chaos-Based Image Encryption: Review, Application, and Challenges (2023) · cites this paper as [131]
The resulting encoded sequence of [131] is used as an image-encryption scheme, providing greater resistance to a variety of attacks.
“The resulting encoded sequence is then used as an image-encryption scheme, providing greater resistance to a variety of attacks.”
From Chaos-Based Image Encryption: Review, Application, and Challenges (2023) · cites this paper as [131]
Cites
Chen, M., Mao, S., & Liu, Y. (2014) · Mobile Networks and Applications · doi:10.1007/s11036-013-0489-0 · Cited by 2,913 · see citing works
Cosine-transform-based chaotic system for image encryption
Hua, Z., Zhou, Y., & Huang, H. (2019) · Information Sciences · doi:10.1016/j.ins.2018.12.048 · Cited by 776 · see citing works
Self-citationIoT Privacy and Security: Challenges and Solutions
Tawalbeh, L., Muheidat, F., Tawalbeh, M., & Quwaider, M. (2020) · Applied Sciences · doi:10.3390/app10124102 · Cited by 764 · see citing works
Depreciating motivation and empirical security analysis of chaos-based image and video encryption
Preishuber, M., Hutter, T., Katzenbeisser, S., & Uhl, A. (2018) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2018.2812080 · Cited by 256 · see citing works
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Cited by
Sorted by citations
Chaos-Based Image Encryption: Review, Application, and Challenges
Zhang, B., & Liu, L. (2023) · Mathematics · doi:10.3390/math11112585 · Cited by 328 · see citing works
“[131] constructed a discrete mHR model to produce four sets of hidden chaotic burst sequences, which are subsequently encoded using their ISIs.”
Method
- Authors
- Bao, H., Hua, Z., Liu, W., & Bao, B.
- Affiliations
- Nanjing University of Aeronautics and Astronautics (CN)
- Harbin Institute of Technology (CN)
- Changzhou University (CN)
- Corresponding author
- Bao, Han
- Venue
- Science China Technological Sciences
- Published
- 2021-08-11
- Volume
- 64
- Issue
- 10
- Pages
- 2281-2291
- ISSN
- 1869-1900 (ISSN-L)
- 1674-7321
- OpenAlex
- W3194998923
- Cited by
- 85
- References
- 4
- Citations by year
- 2026: 5 · 2025: 18 · 2024: 20 · 2023: 25 · 2022: 15 · 2021: 2
- FWCI
- 5.39
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Science China Technological Sciences (publishedVersion)
- Topics
- 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
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Memory and Neural Computing
- Keywords
- chaotic bursting, image encryption, chaotic sequences, symmetric encryption, bidirectional diffusion, attack robustness
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.
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