Open Access
Issue |
E3S Web Conf.
Volume 616, 2025
2nd International Conference on Renewable Energy, Green Computing and Sustainable Development (ICREGCSD 2025)
|
|
---|---|---|
Article Number | 02009 | |
Number of page(s) | 15 | |
Section | Green Computing | |
DOI | https://doi.org/10.1051/e3sconf/202561602009 | |
Published online | 24 February 2025 |
- D.G. Lowe Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision 60, 91 (2004). [CrossRef] [Google Scholar]
- D.G. Lowe Distinctive image features from scale-invariant keypoints, International Journal of Computer Vision 60, 91 (2004). [CrossRef] [Google Scholar]
- J. Li, X. Li, Y. Zhu Segmentation-based image copy-move forgery detection scheme, IEEE Transactions on Information Forensics and Security 10, 507 (2015). [CrossRef] [Google Scholar]
- Q. Wang, R. Zhuang, X. Wang Detecting image forgery using adaptive over segmentation and feature point matching, IEEE Transactions on Information Forensics and Security 12, 1242 (2016). [Google Scholar]
- I. Amerini, L. Ballan, R. Caldelli, A.D. Bimbo A sift-based forensic method for copymove attack detection and transformation recovery, IEEE Transactions on Information Forensics and Security 6, 1099 (2011). [CrossRef] [Google Scholar]
- J. Kim, V.H. Huynh-Thu, M. Savvides Attention-based image forgery detection and localization using deep learning, Journal of Visual Communication and Image Representation 53, 103 (2018). [Google Scholar]
- L. Yi, J. Liu, D. Zhao Detection of copy-move forgery in digital images based on improved surf algorithm, Journal of Multimedia Tools and Applications 77, 8921 (2018). [Google Scholar]
- H. Farid Image forgery detection, IEEE Signal Processing Magazine 26, 16 (2009). [CrossRef] [Google Scholar]
- N. Satheesh, J.B. Dennis et al. Routing with cooperative nodes using improved learning approaches, Intelligent Automation and Soft Computing 35, 2857 (2023). [CrossRef] [Google Scholar]
- Z. Li Emerging deep learning techniques for image forgery detection: A review, IEEE Access 7, 93757 (2019). [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.