Name
Title | ||
|---|---|---|
Camera model identification based machine learning approach with high order statistics features. |
Abstract | ||
|---|---|---|
Source camera identification methods aim at identifying the camera used to capture an image. In this paper we developed a method for digital camera model identification by extracting three sets of features in a machine learning scheme. These features are the co-occurrences matrix, some features related to CFA interpolation arrangement, and conditional probability statistics. These features give high order statistics which supplement and enhance the identification rate. The method is implemented with 14 camera models from Dresden database with multi class SVM classifier. A comparison is performed between our method and a camera fingerprint correlation-based method which only depends on PRNU extraction. The experiments prove the strength of our proposition since it achieves higher accuracy than the correlation-based method. |
Year | Venue | Keywords |
|---|---|---|
2016 | European Signal Processing Conference | Camera identification,Co-occurrences,CFA interpolation,Conditional Probability,SVM |
Field | DocType | ISSN |
Camera auto-calibration,Interpolation,Artificial intelligence,Order statistic,System identification,Computer vision,Pattern recognition,Support vector machine,Feature extraction,Fingerprint,Digital camera,Mathematics,Machine learning | Conference | 2076-1465 |
Citations | PageRank | References |
1 | 0.38 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amel Tuama | 1 | 1 | 0.38 |
| Frederic Comby | 2 | 73 | 11.55 |
| Marc Chaumont | 3 | 172 | 20.40 |