Name
Abstract | ||
|---|---|---|
Source camera identification finds many applications in real world. Although many identification methods have been proposed, they work with only a small set of cameras, and are weak at identifying cameras of the same model. Based on the observation that a digital image would not change if the same Auto-White Balance (AWB) algorithm is applied for the second time, this paper proposes to identify the source camera by approximating the AWB algorithm used inside the camera. To the best of our knowledge, this is the first time that a source camera identification method based on AWB has been reported. Experiments show near perfect accuracy in identifying cameras of different brands and models. Besides, proposed method performances quite well in distinguishing among camera devices of the same model, as AWB is done at the end of imaging pipeline, any small differences induced earlier will lead to different types of AWB output. Furthermore, the performance remains stable as the number of cameras grows large. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICCV.2011.6126225 | ICCV |
Keywords | Field | DocType |
auto-white balance approximation,source camera identification,source camera identification method,image processing,identification,awb algorithm,different type,source camera,cameras,imaging pipeline,camera device,identification method,proposed method performance,different brand,digital image,awb output,feature extraction,accuracy,measurement | Computer vision,Digital camera back,Pattern recognition,Image sensor,Computer science,Camera auto-calibration,Smart camera,Camera resectioning,Artificial intelligence,Image resolution,Pinhole camera model,Three-CCD camera | Conference |
ISSN | ISBN | Citations |
1550-5499 | 978-1-4577-1101-5 | 8 |
PageRank | References | Authors |
0.50 | 19 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhonghai Deng | 1 | 8 | 0.50 |
| Arjan Gijsenij | 2 | 792 | 33.96 |
| Jingyuan Zhang | 3 | 653 | 60.53 |