Name
Abstract | ||
|---|---|---|
Source camera identification forensics aims at determining and authenticating the original sources of digital images to support forensics and get the trace of digital images. This paper introduces a new wavelet features based passive forensic method for the identification of the image source camera. We consider the intrinsic defects and processing of imaging pipeline within digital cameras can be used to formulate the source camera identification problem. Based on this idea, we extract higher-order wavelet features and wavelet coefficient co-occurrence features from taken images, and then apply sequential forward feature selection (SFFS) method to reduce the redundancy and correlation of features and finally use multi-class support vector machine (multi-class SVM) as classifier to identify source cameras. The effectiveness of the proposed approach, also in comparison with other approaches, is experimentally proved on images of six digital cameras. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/IIH-MSP.2009.244 | IIH-MSP |
Keywords | Field | DocType |
source camera identification forensics,higher-order wavelet feature,passive forensic method,source camera identification,feature correlation reduction,feature redundancy reduction,image source camera,higher-order wavelet feature extraction,original source,wavelet transforms,source camera identification forensic,digital camera,sequential forward feature selection,digital image source camera,data,new wavelet,support vector machine classifier,source camera,authentication,source camera identification problem,feature extraction,image classification,trajectorie,cameras,wavelet features,sffs method,digital image,message authentication,pipeline imaging processing,multiclass svm,wavelet coefficient co-occurrence feature,support vector machines,higher order,support vector machine,forensics,accuracy,feature selection | Computer vision,Feature selection,Pattern recognition,Computer science,Support vector machine,Feature extraction,Digital image,Digital camera,Artificial intelligence,Contextual image classification,Wavelet transform,Wavelet | Conference |
ISBN | Citations | PageRank |
978-0-7695-3762-7 | 6 | 0.49 |
References | Authors | |
9 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Wang | 1 | 94 | 28.27 |
| Yiping Guo | 2 | 6 | 0.49 |
| Xiangwei Kong | 3 | 387 | 37.93 |
| Fanjie Meng | 4 | 23 | 4.93 |