Name
Abstract | ||
|---|---|---|
As smartphones are being widely used in daily lives, the images captured by smartphones become ubiquitous and may be used for legal purposes. Accordingly, the authentication of smartphone images and the identification of post-capture manipulation are of significant interest in digital forensics. In this paper, we propose a method to determine the smartphone camera source of a particular image and operations that may have been performed on that image. We first take images using different smartphones and purposely manipulate the images, including different combinations of double JPEG compression, cropping, and rescaling. Then, we extract the marginal density in low frequency coordinates and neighboring joint density features on intra-block and inter-block as features. Finally, we employ a support vector machine to identify the smartphone source as well as to reveal the operations. Experimental results show that our method is very promising for identifying both smartphone source and manipulations. Our study also indicates that applying unsupervised clustering and supervised classification together (clustering first, followed by classification) leads to improvement in identifying smartphone sources and manipulations and thus provides a means to address the complexity issue of intentional manipulation. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-31087-4_28 | IEA/AIE |
Keywords | Field | DocType |
smartphone-image source,smartphone source,marginal density,different smartphones,joint density feature,intentional manipulation,post-capture manipulation,smartphone camera source,particular image,smartphone image,different combination | Hierarchical clustering,Computer vision,Authentication,Digital forensics,Computer science,Support vector machine,Image forensics,JPEG,Artificial intelligence,Jpeg compression,Cluster analysis | Conference |
Citations | PageRank | References |
4 | 0.44 | 14 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingzhong Liu | 1 | 588 | 44.77 |
| Xiaodong Li | 2 | 4 | 0.44 |
| Lei Chen | 3 | 35 | 8.97 |
| Hyuk Cho | 4 | 213 | 16.91 |
| Peter A. Cooper | 5 | 13 | 2.53 |
| Zhongxue Chen | 6 | 244 | 15.77 |
| Mengyu Qiao | 7 | 263 | 17.16 |
| Andrew H. Sung | 8 | 1034 | 84.10 |