Protection of image ROI using chaos-based encryption and DCNN-based object detection
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?
Song [195] employed the YOLOv4 target detection model to identify regions of interest and created a novel image-encryption algorithm using chaotic mapping that aims to protect local images.
“Song [195] employed the YOLOv4 target detection model to identify the regions of interest and created a novel image-encryption algorithm using chaotic mapping.”
From Chaos-Based Image Encryption: Review, Application, and Challenges (2023) · cites this paper as [195]
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
Chen, J., Zhu, Z.-L., Zhang, L.-B., Zhang, Y., & Yang, B.-Q. (2018) · Signal Processing · doi:10.1016/j.sigpro.2017.07.034 · Cited by 320 · see citing works
Flagged first, then by citations
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
“Thus, the selective encryption of image content is a relatively new and promising research direction [195–197].”
BackgroundMethod
- Authors
- Song, W., Fu, C., Zheng, Y., Cao, L., Tie, M., & Sham, C.-W.
- Affiliations
- Northeastern University (CN)
- Chinese University of Hong Kong (HK)
- Beijing Information Science & Technology University (CN)
- University of Auckland (NZ)
- Corresponding author
- Fu, Chong
- Venue
- Neural Computing and Applications
- Published
- 2022-01-12
- Volume
- 34
- Issue
- 7
- Pages
- 5743-5756
- ISSN
- 0941-0643 (ISSN-L)
- 1433-3058
- OpenAlex
- W4205210078
- Cited by
- 64
- References
- 3
- Citations by year
- 2026: 5 · 2025: 7 · 2024: 18 · 2023: 21 · 2022: 13
- FWCI
- 4.83
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neural Computing and Applications (publishedVersion)
- Funders
- National Natural Science Foundation of China (61773068)
- Fundamental Research Funds for the Central Universities (N2024005-1)
- National Key R&D Program of China (2021YFF0306405)
- 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, chaos-based cryptography, YOLOv4, object detection, privacy 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
