Name
Abstract | ||
|---|---|---|
Source camera identification enables forensic investigator to discover the probable source model that are employed to acquire the image under investigation. It is important whenever digital content is presented as a silent witness. In this paper, we present a source camera identification method via image texture features that are extracted from well selected color model and color channel. Except to distinguish source camera models from images whatever they are captured via same or different brand cameras, the main contributions of the proposed method are as follows: (1) It can distinguish imaging device individuals from images even if they are taken by using same brand and model of devices. (2) It is robust for content-preserving manipulations or geometric distortions, such as JEPG compression, adding noise, and rotation and scaling. The experimental results demonstrate that the performance of the proposed method is satisfactory. Compared with the state-of-the-art methods, the proposed method is superior in both detection accuracy and robustness. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.neucom.2016.05.012 | Neurocomputing |
Keywords | Field | DocType |
Source camera identification,Local Binary Pattern (LBP),Local Phase Quantization (LPQ),Support vector machine (SVM) | Computer science,Source model,Robustness (computer science),Color model,Artificial intelligence,Scaling,Digital content,Computer vision,Pattern recognition,Image texture,Camera identification,Machine learning,Channel (digital image) | Journal |
Volume | Issue | ISSN |
207 | C | 0925-2312 |
Citations | PageRank | References |
11 | 0.48 | 19 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bingchao Xu | 1 | 11 | 0.48 |
| Xiaofeng Wang | 2 | 94 | 9.88 |
| Xiaorui Zhou | 3 | 41 | 1.53 |
| Jianghuan Xi | 4 | 13 | 0.85 |
| Shangping Wang | 5 | 57 | 13.40 |