Abstract: We propose a fully automatic spliced image detection method based on consistency checking of camera characteristics among different areas in an image. A test image is first segmented into distinct areas. One camera response function (CRF) is estimated from each area using geometric invariants from locally planar irradiance points (LPIPs). To classify a boundary segment between two areas as authentic or spliced, CRF cross fitting scores and area intensity features are computed and fed to SVM-based classifiers. Such segment-level scores are further fused to form the image-level decision. Tests on both the benchmark data set and an unseen high-quality spliced data set reach promising performance levels with 70% precision and 70% recall.

  url          = {http://www.ee.columbia.edu/dvmm/publications/07/Jessie_icme07.pdf},
  booktitle    = {International Conference on Multimedia and Expo},
  author       = {Yu-Feng Hsu and Shih-Fu Chang},
  location     = {Beijing, China},
  year         = {2007},
  title        = {Image splicing detection using camera response function consistency and automatic segmentation},