Abstract: In this paper, we extend our camera identification technology based on sensor noise to a more general setting when the image under investigation has been simultaneously cropped and scaled. The sensor fingerprint detection is formulated using hypothesis testing as a two-channel problem and a detector is derived using the generalized likelihood ratio test. A brute force search is proposed to find the scaling factor which is then refined in a detailed search. The cropping parameters are determined from the maximum of the normalized cross-correlation between two signals. The accuracy and limitations of the proposed technique are tested on images that underwent a wide range of cropping and scaling, including images that were acquired by digital zoom. Additionally, we demonstrate that sensor noise can be used as a template to reverse-engineer in-camera geometrical processing as well as recover from later geometrical transformations, thus offering a possible application for re-synchronizing in digital watermark detection.

  publisher    = {SPIE},
  author       = {Miroslav Goljan and Jessica Fridrich},
  url          = {http://www.ws.binghamton.edu/fridrich/Research/Crop_scale.pdf},
  journal      = {Proc. SPIE, Electronic Imaging, Security, Steganography, and Watermarking of Multimedia Contents X},
  volume       = {6819},
  year         = {2008},
  title        = {Camera identification from cropped and scaled images},
  pages        = {68190E},