Digital Watermarking

Volume 5703 of the series Lecture Notes in Computer Science pp 294-307

Camera-Model Identification Using Markovian Transition Probability Matrix

  • Guanshuo XuAffiliated withCarnegie Mellon UniversityNew Jersey Institute of Technology
  • , Shang GaoAffiliated withCarnegie Mellon UniversityNew Jersey Institute of TechnologyWuhan University
  • , Yun Qing ShiAffiliated withCarnegie Mellon UniversityNew Jersey Institute of Technology
  • , RuiMin HuAffiliated withCarnegie Mellon UniversityWuhan University
  • , Wei SuAffiliated withCarnegie Mellon UniversityUS Army CERDEC

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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.


Camera Identification Markov Process Transition Probability Matrix