Blockchain based Chaotic Deep GAN Encryption scheme for securing medical images in a cloud environment
Integrity signals
Registry
Cites
Statistics
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?
Ref. [150] proposed a blockchain-based Chaotic Deep Generative Adversarial Network Encryption (BCDGE) scheme with a cloud storage system for securing medical images.
“Ref. [150] proposed a blockchain-based Chaotic Deep Generative Adversarial Network Encryption (BCDGE) scheme with a cloud storage system for securing medical images.”
From Chaos-Based Image Encryption: Review, Application, and Challenges (2023) · cites this paper as [150]
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
“In [150,151], the authors chose to introduce cloud storage or cloud computing into encryption algorithms.”
Background
- Authors
- Neela, K. L., & Kavitha, V.
- Corresponding author
- Neela, K. L.
- Venue
- Applied Intelligence
- Published
- 2022-06-15
- Volume
- 53
- Issue
- 4
- Pages
- 4733-4747
- ISSN
- 0924-669X (ISSN-L)
- 1573-7497
- OpenAlex
- W4282923875
- Cited by
- 42
- References
- 0
- Citations by year
- 2026: 8 · 2025: 11 · 2024: 18 · 2023: 5
- FWCI
- 2.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Applied Intelligence (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Chaos-based Image/Signal Encryption
- Physical Sciences › Computer Science › Information Systems › Blockchain Technology Applications and Security
- Physical Sciences › Computer Science › Information Systems › Cloud Data Security Solutions
- Keywords
- medical image encryption, blockchain, deep generative adversarial networks, chaos-based cryptography, cloud storage security, confusion and diffusion, electronic health records
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.
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