Name
Abstract | ||
|---|---|---|
Current prominent camera identification methods use wavelet-based filter to extract photo-response non-uniformity (PRNU) noise as camera fingerprint. However, these noise features in heavily textured images can not be extracted by using wavelet-based filter effectively. In this paper, we propose a new camera identification method that uses curvelet-based filter to extract noise features in heavily textured images or non-heavily textured image. Because curvelet transform allows an optimal sparse representation of objects with C2 singularities, curvelet-based filter can extract the noise features in heavily textured images more effectively than wavelet-based filter. To increase the recognition rate for heavily textured images, we differentiate heavily textured images from non-heavily textured images by using the bivariate kurtosis of an image, and Neyman-Pearson decision is used to determine different decision thresholds. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICIP.2010.5652078 | ICIP |
Keywords | Field | DocType |
photo-response nonuniformity noise extraction,optimal sparse representation,image representation,curvelet-based filter,image sensor,curvelet transforms,bivariate kurtosis,wavelet transforms,digital forensics,prnu noise,curvelet transform,image recognition,camera identification,decision thresholds,feature extraction,color image origin identification,heavily textured images,neyman-pearson decision,camera identification method,image texture,recognition rate,wavelet-based filter,camera fingerprint,image colour analysis,noise feature extraction,noise,sparse representation,correlation,color image | Computer vision,Image sensor,Pattern recognition,Computer science,Image texture,Sparse approximation,Feature extraction,Artificial intelligence,Curvelet,Wavelet transform,Color image,Wavelet | Conference |
ISSN | ISBN | Citations |
1522-4880 E-ISBN : 978-1-4244-7993-1 | 978-1-4244-7993-1 | 0 |
PageRank | References | Authors |
0.34 | 5 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chi Zhang | 1 | 192 | 40.36 |
| Hong-Jiang ZHANG | 2 | 17378 | 1393.22 |