An efficient image encryption using deep neural network and chaotic map
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?
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 [52]
Cites
When an attacker meets a cipher-image in 2018: A year in review
Li, C., Zhang, Y., & Xie, E. Y. (2019) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2019.102361 · Cited by 217 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A Faster and Robust Artificial Neural Network Based Image Encryption Technique With Improved SSIM
Panigrahy, A. K., Maniyath, S. R., Sathiyanarayanan, M., Dholvan, M., Ramaswamy, T., Hanumanthakari, S., Vignesh, N. A., Kanithan, S., & Swain, R. (2024) · IEEE Access · doi:10.1109/access.2024.3353294 · Cited by 35 · see citing works
Self-citationSurvey on medical image encryption: From classical to deep learning-based approaches
Prasad, S., & Singh, A. K. (2025) · Computers and Electrical Engineering · doi:10.1016/j.compeleceng.2024.110011 · Cited by 21 · see citing works
Subathra, S., & Thanikaiselvan, V. (2025) · Scientific Reports · doi:10.1038/s41598-025-27080-z · Cited by 4 · see citing works
“To test the sequence, we generate 5000 sequences with length of 2 20 .”
Self-citation
- Authors
- Maniyath, S. R., & V, T.
- Affiliations
- Vellore Institute of Technology University (IN)
- Venue
- Microprocessors and Microsystems
- Publisher
- Elsevier BV
- Published
- 2020-09
- Volume
- 77
- Pages
- 103134
- ISSN
- 0141-9331 (ISSN-L)
- OpenAlex
- W3026554779
- Cited by
- 105
- References
- 1
- Citations by year
- 2026: 13 · 2025: 16 · 2024: 28 · 2023: 20 · 2022: 18 · 2021: 6 · 2020: 4
- FWCI
- 4.67
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Microprocessors and Microsystems (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Chaos-based Image/Signal Encryption
- Physical Sciences › Computer Science › Artificial Intelligence › Computer Science and Engineering
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Keywords
- image encryption, deep neural network, chaotic maps, image security, attack resistance, image quality preservation
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.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
