Name
Abstract | ||
|---|---|---|
In this paper, we revisit the problem of digital camera sensor identification using photo-response non-uniformity noise (PRNU). Considering the identification task as a joint estimation and detection problem, we use a simplified model for the sensor output and then derive a Maximum Likelihood estimator of the PRNU. The model is also used to design optimal test statistics for detection of PRNU in a specific image. To estimate unknown shaping factors and determine the distribution of the test statistics for the image-camera match, we construct a predictor of the test statistics on small image blocks. This enables us to obtain conservative estimates of false rejection rates for each image under Neyman-Pearson testing. We also point out a few pitfalls in camera identification using PRNU and ways to overcome them by preprocessing the estimated PRNU before identification. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1117/12.703370 | PROCEEDINGS OF THE SOCIETY OF PHOTO-OPTICAL INSTRUMENTATION ENGINEERS (SPIE) |
Keywords | Field | DocType |
design optimization,maximum likelihood estimate,sensors,digital image,digital imaging | Fixed-pattern noise,Computer vision,Digital forensics,Test statistic,Maximum likelihood,Preprocessor,Digital camera,Digital imaging,Artificial intelligence,Statistical hypothesis testing,Mathematics | Conference |
Volume | ISSN | Citations |
6505 | 0277-786X | 44 |
PageRank | References | Authors |
3.47 | 8 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| mo chen | 1 | 189 | 12.84 |
| Jessica Fridrich | 2 | 8014 | 592.05 |
| Miroslav Goljan | 3 | 2430 | 221.88 |