A separable reversible data hiding scheme for encrypted images based on Tromino scrambling and adaptive pixel value ordering
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Pixel scrambling preserves the histogram of the original image because it does not alter pixel intensity.
“In particular, this pixel scrambling process preserves the histogram of the original image since it does not alter the pixel intensity13.”
From Enhancing image security via chaotic maps, Fibonacci, Tribonacci transformations, and DWT diffusion: a robust data encryption approach (2024) · cites this paper as [13]
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Hazzazi, M. M., Rehman, M. U., Shafique, A., Aljaedi, A., Bassfar, Z., & Usman, A. B. (2024) · Scientific Reports · doi:10.1038/s41598-024-62260-3 · Cited by 25 · see citing works
- Authors
- Long, M., Zhao, Y., Zhang, X., & Peng, F.
- Affiliations
- Changsha University of Science and Technology (CN)
- Hunan University (CN)
- Corresponding author
- Peng, Fei
- Venue
- Signal Processing
- Publisher
- Elsevier BV
- Published
- 2020-11
- Volume
- 176
- Pages
- 107703
- ISSN
- 0165-1684 (ISSN-L)
- OpenAlex
- W3038706812
- Cited by
- 25
- References
- 0
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 2 · 2023: 3 · 2022: 7 · 2021: 8 · 2020: 1
- FWCI
- 1.85
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Signal Processing (publishedVersion)
- Funders
- National Natural Science Foundation of China
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Chaos-based Image/Signal Encryption
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Data Compression Techniques
- Keywords
- reversible data hiding, encrypted images, prediction error histogram, image encryption, embedding capacity, visual quality
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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