Name
Abstract | ||
|---|---|---|
This paper presents a new approach of camera model identification. It is based on using the noise residual extracted from an image by applying a wavelet-based denoising filter in a machine learning framework. We refer to this noise residual as the polluted noise (POL-PRNU), because it contains a PRNU signal contaminated with other types of noise such as the image content. Our proposition consists of extracting high order statistics from POL-PRNU by computing co-occurrences matrix. Additionally, we enrich the set of features with those related to CFA demosaicing artifacts. These two sets of features feed a classifier to perform a camera model identification. The experimental results illustrate the fact that machine learning techniques with discriminant features are efficient for camera model identification purposes. |
Year | Venue | Field |
|---|---|---|
2015 | IWDW | Noise reduction,Computer vision,Residual,Pattern recognition,Computer science,Demosaicing,Feature extraction,Artificial intelligence,Order statistic,System identification,Classifier (linguistics),Wavelet |
DocType | Citations | PageRank |
Conference | 4 | 0.43 |
References | Authors | |
7 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Amel Tuama | 1 | 4 | 0.43 |
| Frederic Comby | 2 | 73 | 11.55 |
| Marc Chaumont | 3 | 172 | 20.40 |