Skip to Main content Skip to Navigation
Journal articles

Exposing image resampling forgery by using linear parametric model

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.
Document type :
Journal articles
Complete list of metadatas

https://hal-utt.archives-ouvertes.fr/hal-02316536
Contributor : Jean-Baptiste Vu Van <>
Submitted on : Tuesday, October 15, 2019 - 1:48:38 PM
Last modification on : Friday, July 17, 2020 - 8:32:02 PM

Identifiers

Collections

CNRS | ROSAS | UTT

Citation

Tong Qiao, Aichun Zhu, Florent Retraint. Exposing image resampling forgery by using linear parametric model. Multimedia Tools and Applications, Springer Verlag, 2018, 77 (2), pp.1501-1523. ⟨10.1007/s11042-016-4314-1⟩. ⟨hal-02316536⟩

Share

Metrics

Record views

34