Abstract: This paper adds a new perspective to the analysis and detection of periodic interpolation artifacts in resized digital images. Instead of relying on a single, global predictor, we discuss how the specific structure of resized images can be explicitly modeled by a series of linear predictors. Characteristic periodic correlations between neighboring pixels are then measured in the estimated predictor coefficients itself. Experimental results on a large database of images suggest a superior detection performance compared to state-of-the-art methods.

  url          = {http://dud.inf.tu-dresden.de/~kirchner/Documents/MMSec2010_paper.pdf},
  booktitle    = {ACM Workshop on Multimedia and Security},
  author       = {Matthias Kirchner},
  location     = {Rome, Italy},
  year         = {2010},
  title        = {Linear row and column predictors for the analysis of resized images},
  pages        = {13-18},