Name
Abstract | ||
|---|---|---|
To deal with the problem of image forgery detection, many forensic tools have been proposed. Most existing tools perform efficiently in a supervised scenario with a large training set while their statistical performances cannot be analytically established. Also, limited research proposes statistical model-based detectors for image forensics, especially for image splicing forgery detection. Therefore, in this paper, we propose a training-free forensic detector with analytically statistical performance for splicing forgery detection. The detector is designed based on a simplified noise model of JPEG image which assumes the variance of pixels as a quadratic function of the expectation of pixels. The proposed simplified noise model is characterized by two parameters which can serve as camera fingerprints for image forgery detection. By employing the framework of hypothesis testing theory, a training-free Generalized Likelihood Ratio Test (GLRT) is designed, which ensures the high detection performance for a prescribed false alarm rate. Besides, detection threshold can be configured in the fashion independent of image content. Numerical results validate the accuracy of the simplified noise model and the effectiveness of the proposed detector. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1016/j.image.2022.116785 | Signal Processing: Image Communication |
Keywords | DocType | Volume |
Image splicing detection,Noise model,Camera fingerprints,Hypothesis testing | Journal | 107 |
ISSN | Citations | PageRank |
0923-5965 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yanli Chen | 1 | 0 | 0.34 |
| Florent Retraint | 2 | 0 | 0.34 |
| Tong Qiao | 3 | 0 | 0.68 |