Name
Abstract | ||
|---|---|---|
Image forensics, besides understanding if a digital image has been forged, often aims at determining information about image origin. In particular, it could be worthy to individuate which is the kind of source (digital camera, scanner or computer graphics software) that has generated a certain photo. Such an issue has already been studied in literature, but the problem of doing that in a blind manner has not been faced so far. It is easy to understand that in many application scenarios information at disposal is usually very limited; this is the case when, given a set of L images, the authors want to establish if they belong to K different classes of acquisition sources, without having any previous knowledge about the number of specific types of generation processes. The proposed system is able, in an unsupervised and fast manner, to blindly classify a group of photos without neither any initial information about their membership nor by resorting at a trained classifier. Experimental results have been carried out to verify actual performances of the proposed methodology and a comparative analysis with two SVM-based clustering techniques has been performed too. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1049/iet-ipr.2014.0316 | Image Processing, IET |
Keywords | Field | DocType |
image classification,image forensics,pattern clustering,svm-based clustering techniques,acquisition source identification,acquisition sources,blind image classification,digital image,generation processes,image origin,initial information | Computer vision,Automatic image annotation,Pattern recognition,Feature detection (computer vision),Computer science,Image processing,Digital image,Artificial intelligence,Cluster analysis,Contextual image classification,Digital image processing,Computer graphics | Journal |
Volume | Issue | ISSN |
9 | 4 | 1751-9659 |
Citations | PageRank | References |
4 | 0.39 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Irene Amerini | 1 | 325 | 21.16 |
| Rudy Becarelli | 2 | 106 | 5.97 |
| Bertini, B. | 3 | 4 | 0.39 |
| Roberto Caldelli | 4 | 481 | 37.01 |