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Multimedia Tools and Applications

, Volume 77, Issue 2, pp 1501–1523 | Cite as

Exposing image resampling forgery by using linear parametric model

  • Tong Qiao
  • Aichun Zhu
  • Florent Retraint
Article
  • 389 Downloads

Abstract

Resampling forgery generally refers to as the technique that utilizes interpolation algorithm to maliciously geometrically transform a digital image or a portion of an image. This paper investigates the problem of image resampling detection based on the linear parametric model. First, we expose the periodic artifact of one-dimensional 1-D) resampled signal. After dealing with the nuisance parameters, together with Bayes’ rule, the detector is designed based on the probability of residual noise extracted from resampled signal using linear parametric model. Subsequently, we mainly study the characteristic of a resampled image. Meanwhile, it is proposed to estimate the probability of pixels’ noise and establish a practical Likelihood Ratio Test (LRT). Comparison with the state-of-the-art tests, numerical experiments show the relevance of our proposed algorithm with detecting uncompressed/compressed resampled images.

Keywords

Image resampling forensics Linear parametric model Bayes’ rule Hypothesis testing 

Notes

Acknowledgments

This work is funded by the State Key Program of Zhejiang Province Natural Science Foundation of China under Grant No. LZ15F020003 and the Natural Science Foundation of China (No. 61602295) and the Natural Science Foundation of Shanghai (No. 16ZR1413100). The Ph.D thesis of Tong Qiao is funded by the China Scholar Council (CSC) and the region Champagne-Ardenne, IDENT project.

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of CyberspaceHangzhou Dianzi UniversityHangzhouChina
  2. 2.School of Computer Science and TechnologyNanjing Tech UniversityNanjingChina
  3. 3.LM2SUniversity of Technology of TroyesTroyesFrance

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