Paper Info
Title
Camera-Model Identification Using Markovian Transition Probability Matrix
Abstract
Detecting the (brands and) models of digital cameras from given digital images has become a popular research topic in the field of digital forensics. As most of images are JPEG compressed before they are output from cameras, we propose to use an effective image statistical model to characterize the difference JPEG 2-D arrays of Y and Cb components from the JPEG images taken by various camera models. Specifically, the transition probability matrices derived from four different directional Markov processes applied to the image difference JPEG 2-D arrays are used to identify statistical difference caused by image formation pipelines inside different camera models. All elements of the transition probability matrices, after a thresholding technique, are directly used as features for classification purpose. Multi-class support vector machines (SVM) are used as the classification tool. The effectiveness of our proposed statistical model is demonstrated by large-scale experimental results.
Year
DOI
Venue
2009
10.1007/978-3-642-03688-0_26
IWDW
Keywords
Field
DocType
probability matrix,digital forensics,proposed statistical model,digital image,2-d array,effective image,image difference jpeg,difference jpeg,camera-model identification,image formation pipeline,markovian transition,digital camera,jpeg image,model identification,transition probability,model specification,markov process,statistical model,support vector machine,image formation
Computer vision,Pattern recognition,Stochastic matrix,Computer science,Support vector machine,Image formation,Digital image,JPEG,Statistical model,Artificial intelligence,Thresholding,System identification
Conference
Volume
ISSN
Citations 
5703
0302-9743
8
PageRank 
References 
Authors
0.74
7
5
Name
Order
Citations
PageRank
Guanshuo Xu11245.26
Shang Gao229159.33
Yun Qing Shi351823.34
Ruimin Hu4961117.18
Wei Su562938.52