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Robust median filtering detection based on the difference of frequency residuals

  • Wenjie Li
  • Rongrong NiEmail author
  • Xiaolong Li
  • Yao ZhaoEmail author
Article
  • 43 Downloads

Abstract

Recently, the detection of median filtering (MF), which is a popular nonlinear denoising manipulation, has attracted extensive attention from researchers. Several detectors with satisfying performance have been developed, while most of them need to train proper classifiers and their performance may be degraded under JPEG compression. In this paper, a training-free MF detector with single-dimensional feature is proposed based on the difference of frequency residuals, which can solve the detection issue of median filtering images under JPEG post-processing. It is designed relying on the fact that when an image is median filtered over and over again, the frequency residual obtained from continuous two images monotonically decreases. The difference between the frequency residuals obtained from the first MF and the second MF is pretty large in an unfiltered test image, while it is relatively small if the test image is a median filtered one. Thus, the unfiltered and the median filtered images are distinguishable. Furthermore, a novel strategy combining unsharp masking (USM) sharpening is implemented to suppress the effect of image content and find a universal threshold which is utilized to classify two types of images. Experimental results show that the proposed method outperforms some state-of-the-art methods at the condition of a low false alarm rate, especially when the test images are in low quality and low resolution.

Keywords

Digital image forensics Median filtering JPEG compression USM sharpening False alarm rate 

Notes

Acknowledgments

This work was supported in part by the National Key Research and Development of China (2018YFC0807306), National NSF of China (61672090, 61332012, 61532005), and Fundamental Research Funds for the Central Universities (2018JBZ001).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Information ScienceBeijing Jiaotong UniversityBeijingChina
  2. 2.Beijing Key Laboratory of Advanced Information Science and Network TechnologyBeijingChina

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