Name
Abstract | ||
|---|---|---|
Scale invariant feature transform (SIFT), as one of the most popular local feature extraction algorithms, has been widely employed in many computer vision and multimedia security applications. Although SIFT has been extensively investigated from various perspectives, its security against malicious attacks has rarely been discussed. In this paper, we show that the SIFT keypoints can be effectively ... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TIFS.2016.2553645 | IEEE Transactions on Information Forensics and Security |
Keywords | Field | DocType |
Feature extraction,Detectors,Distortion,Security,Optimization,Multimedia communication,Algorithm design and analysis | Computer vision,Scale-invariant feature transform,Algorithm design,Pattern recognition,Computer science,Scale space,Maxima and minima,Feature extraction,Artificial intelligence,Distortion,Convex relaxation,Detector | Journal |
Volume | Issue | ISSN |
11 | 8 | 1556-6013 |
Citations | PageRank | References |
5 | 0.46 | 34 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yuanman Li | 1 | 27 | 5.86 |
| Jiantao Zhou | 2 | 580 | 78.87 |
| An Cheng | 3 | 7 | 0.83 |
| Xianming Liu | 4 | 461 | 47.55 |
| Yuan Yan Tang | 5 | 10 | 2.23 |