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Exposing image resampling forgery by using linear parametric model

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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.

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Notes

  1. In fact, it is very difficult to assume the accurate distribution model of the original signal without any prior information. In this context, we arbitrarily assume the original signal approximately follows the Uniform distribution in the designed test, which is the limitation of our proposed algorithm.

  2. In fact, it is very difficult to accurately define the distribution of the residual noise from a non-resampled image. Here, it is proposed to arbitrarily approximate its distribution using the Uniform distribution inspired by the reference [21].

  3. The term post-camera is referred to as the operation after acquiring a digital image.

  4. The fixed pattern can be described as α j = (0.5, 0.5, 0.5, 0.5, -0.25, -0.25, -0.25, -0.25) T, where α j denotes the weighted factors (see (15)).

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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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Authors and Affiliations

  1. School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China

    Tong Qiao

  2. School of Computer Science and Technology, Nanjing Tech University, Nanjing, China

    Aichun Zhu

  3. LM2S, University of Technology of Troyes, Troyes, France

    Tong Qiao, Aichun Zhu & Florent Retraint

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  1. Tong Qiao
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  2. Aichun Zhu
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  3. Florent Retraint
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Correspondence to Tong Qiao.

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Qiao, T., Zhu, A. & Retraint, F. Exposing image resampling forgery by using linear parametric model. Multimed Tools Appl 77, 1501–1523 (2018). https://doi.org/10.1007/s11042-016-4314-1

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  • DOI: https://doi.org/10.1007/s11042-016-4314-1

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