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Multidimensional Systems and Signal Processing

, Volume 30, Issue 4, pp 1951–1974 | Cite as

An improved median filtering anti-forensics with better image quality and forensic undetectability

  • Kulbir SinghEmail author
  • Ankush Kansal
  • Gurinder Singh
Article

Abstract

Median filtering has received considerable attention and popularity for image enhancement and anti-forensics. It can be utilized as an image denoising and smoothing tool to disguise the footprints of image processing operations such as image resampling and JPEG compression. A two-step median filtering anti-forensic framework is proposed in this paper to fool the existing median filtering forensic detectors by hiding the median filtering artifacts. In the proposed framework, a variational deconvolution approach is initially employed to generate a median filtered forgery. Now, this forgery is further processed in the second step by considering Total Variation (TV) based minimization optimization problem to eradicate the median filtering artifacts left during deconvolution operation. Moreover, the proposed TV-based minimization algorithms significantly reduce the unnatural (grainy) noise left during the variational deconvolution. Two types of TV-based minimization problems are suggested, first relies on the TV of energy by considering the image gradient and second on the structure of a given image. The performance of the proposed scheme is evaluated by considering the worst-case and optimal scenarios. The experimental results based on UCID and BOSSBase dataset images demonstrate that the proposed anti-forensic methods provide superior results in terms of image visual quality and forensic undetectability as compared to the existing approaches, with slight increase in computational time.

Keywords

Image tampering Image retrieval TV-based minimization Image variational deconvolution Machine learning based forensic detectors 

Notes

Acknowledgements

This work was supported by Visvesvaraya PHD scheme for Electronics and IT, Ministry of Electronics and Information Technology, Government of India (Grant PhD-MLA/4(33)/2015-16/01).

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringTIETPatialaIndia

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