TY - GEN
T1 - Camera-model identification using Markovian transition probability matrix
AU - Xu, Guanshuo
AU - Gao, Shang
AU - Shi, Yun Qing
AU - Hu, Rui Min
AU - Su, Wei
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Camera identification
KW - Markov process
KW - Transition probability matrix
UR - http://www.scopus.com/inward/record.url?scp=70350536695&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70350536695&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-03688-0_26
DO - 10.1007/978-3-642-03688-0_26
M3 - Conference contribution
AN - SCOPUS:70350536695
SN - 3642036872
SN - 9783642036873
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 294
EP - 307
BT - Digital Watermarking - 8th International Workshop, IWDW 2009, Proceedings
T2 - 8th International Workshop on Digital Watermarking, IWDW 2009
Y2 - 24 August 2009 through 26 August 2009
ER -