Name
Abstract | ||
|---|---|---|
In digital image forensics, camera model identification seeks for the source camera model information from the given images under investigation. To achieve this goal, one of the popular approaches is extracting from the images under investigation certain statistical features that capture the difference caused by camera structure and various in-camera image processing algorithms, followed by machine learning and pattern recognition algorithms for similarity measures of extracted features. In this paper, we propose to use uniform gray-scale invariant local binary patterns (LBP) as statistical features. Considering 8-neighbor binary co-occurrence, three groups of 59 local binary patterns are extracted from the spatial domain of red and green color channels, their corresponding prediction-error arrays, and their 1st-level diagonal wavelet sub bands of each image, respectively. Multi-class support vector machines are built for classification of 18 camera models from 'Dresden Image Database'. Compared with the results reported in literatures, the detection accuracy reported in this paper is higher. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/ICME.2012.87 | ICME |
Keywords | Field | DocType |
green color channels,1st-level diagonal wavelet subbands,source camera model information,digital image forensics,uniform gray-scale invariant local binary patterns,various in-camera image processing,wavelet transforms,statistical analysis,gray-scale invariant local binary,local binary pattern,multiclass support vector machines,camera model,lbp,statistical features,feature extraction,image classification,investigation certain statistical feature,cameras,camera structure,local binary patterns,spatial domain,prediction-error arrays,source device identification,camera model classification,dresden image database,red color channels,support vector machines,8-neighbor binary co-occurrence,image colour analysis,camera model identification,accuracy,testing | Computer vision,Pattern recognition,Computer science,Local binary patterns,Camera auto-calibration,Support vector machine,Feature extraction,Artificial intelligence,Digital image processing,Contextual image classification,Channel (digital image),Wavelet transform | Conference |
ISSN | ISBN | Citations |
1945-7871 | 978-1-4673-1659-0 | 17 |
PageRank | References | Authors |
0.65 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guanshuo Xu | 1 | 124 | 5.26 |
| Yun Qing Shi | 2 | 518 | 23.34 |