Abstract: The identification of image acquisition source is an important problem in digital image forensics. In this work, we focus on building a classifier to effectively distinguish between digital images taken from digital single lens reflex (DSLR) and compact cameras. Based on the architecture and the imaging features of DSLR and compact cameras, the images taken from different sources may have different statistical properties in both spatial and transform domains. In this work, we utilized wavelet coefficients and pixel noise statistics to model these two different source classes over 20 different digital cameras. The efficacy of the digital source class identifier, introduced in the paper, has been tested over 1000 high quality camera outputs and post-processed images (resized, re-compressed). Experimental analysis shows that the proposed method has good potential to distinguish DSLR and compact source classes.

  url          = {http://isis.poly.edu/~forensics/pubs/MMSP09final.pdf},
  author       = {Yanmei Fang and Ahmet E. Dirik and Xiaoxi Sun and Nasir D. Memon},
  year         = {2009},
  title        = {Source class identification for {DSLR} and compact cameras},
  booktitle    = {Proceedings of the IEEE International Workshop on Multimedia Signal Processing 2009 (MMSP'09)},