Camera-model identification using Markovian transition probability matrix

Guanshuo Xu, Shang Gao, Yun Qing Shi, Rui Min Hu, Wei Su

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations


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.

Original languageEnglish (US)
Title of host publicationDigital Watermarking - 8th International Workshop, IWDW 2009, Proceedings
Number of pages14
StatePublished - 2009
Event8th International Workshop on Digital Watermarking, IWDW 2009 - Guildford, United Kingdom
Duration: Aug 24 2009Aug 26 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5703 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other8th International Workshop on Digital Watermarking, IWDW 2009
Country/TerritoryUnited Kingdom

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


  • Camera identification
  • Markov process
  • Transition probability matrix


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