Counter-forensics of median filtering

Dang-Nguyen, Duc-Tien; Gebru, Israel D.; Conotter, Valentina; Boato, Giulia; Natale, Francesco G. B. De

doi:10.1109/mmsp.2013.6659298

Cited by 20 publications

(30 citation statements)

References 11 publications

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“…Dang-Nguyen et al [15] proposed another noise attack to the MF image, targeting at forensic detectors in [3]- [5] (see Secs. II-B1-II-B3).…”

Section: ) Dang-nguyen Et Al's Methodmentioning

confidence: 99%

“…Without loss of generality, in this paper, we perform median filtering anti-forensics on images which have been median filtered once, with s = 3. It is also the experimental setting in all the state-of-the-art median filtering anti-forensic methods [13]- [15]. For the sake of clarity, we hereafter use x to denote the original image 2 , and let y = MF (1) (x) denote its corresponding median filtered version.…”

Section: A Image Median Filtering Basicsmentioning

confidence: 99%

“…The achievement of the proposed method is two-fold: (i) improving the visual quality of MF images (with a certain parameter setting) as an image quality enhancement method, and (ii) fooling existing median filtering forensic detectors (with another parameter setting and an additional pixel value perturbation procedure) for anti-forensic purposes. Experimental results demonstrate that the proposed method outperforms current median filtering anti-forensic methods [13]- [15], with a better forensic undetectability against existing forensic detectors [3]- [5], [7], [8], [10]- [12] and a higher visual quality of the processed image. As pointed out in [1], [2], median filtering can also be adopted for disguising traces of other image processing operations.…”

mentioning

confidence: 91%

“…The abundance in literature [3]- [15] indicates that much attention has been drawn to median filtering (anti-)forensics. Yet, median filtering anti-forensics still appears to be a relatively young subject.…”

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confidence: 99%

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Median Filtered Image Quality Enhancement and Anti-Forensics via Variational Deconvolution

Wang

Cayre

et al. 2015

IEEE Trans.Inform.Forensic Secur.

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International audienceMedian filtering enjoys its popularity as a widely adopted image denoising and smoothing tool. It is also used by anti-forensic researchers in helping disguise traces of other image processing operations, e.g., image resampling and JPEG compression. This paper proposes an image variational deconvolution framework for both quality enhancement and anti-forensics of median filtered (MF) images. The proposed optimization-based framework consists of a convolution term, a fidelity term with respect to the MF image, and a prior term. The first term is for the approximation of the median filtering process, using a convolution kernel. The second fidelity term keeps the processed image to some extent still close to the MF image, retaining some denoising or other image processing artifact hiding effects. Using the generalized Gaussian as the distribution model, the last image prior term regularizes the pixel value derivative of the obtained image so that its distribution resembles the original one. Our method can serve as an MF image quality enhancement technique, whose efficacy is validated by experiments conducted on MF images which have been previously “salt & pepper” noised. Using another parameter setting and with an additional pixel value perturbation procedure, the proposed method outperforms the state-of-the-art median filtering anti-forensics, with a better forensic undetectability against existing detectors as well as a higher visual quality of the processed image. Furthermore, the feasibility of concealing image resampling traces and JPEG blocking artifacts is demonstrated by experiments, using the proposed median filtering anti-forensic method

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“…Dang-Nguyen et al [15] proposed another noise attack to the MF image, targeting at forensic detectors in [3]- [5] (see Secs. II-B1-II-B3).…”

Section: ) Dang-nguyen Et Al's Methodmentioning

confidence: 99%

Section: A Image Median Filtering Basicsmentioning

confidence: 99%

mentioning

confidence: 91%

mentioning

confidence: 99%

See 2 more Smart Citations

Median Filtered Image Quality Enhancement and Anti-Forensics via Variational Deconvolution

Wang

Cayre

et al. 2015

IEEE Trans.Inform.Forensic Secur.

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show abstract

“…In other words, median filtering is more likely to put the difference between the adjacent pixels to zero. The characteristic traces of median filtering in uncompressed images are hidden by modifying suitable random pixels based on the occurrence of block center gray level and distribution of block median in [20]. Another approach to conceal traces of median filtering has been presented in [21] which use linear convolution filters of size 3 × 3 to produce another image from the median filtered image such that the fidelity between the median filtered image and that obtained after convolution filtering is maximized.…”

Section: Anti-forensicsmentioning

confidence: 99%

Digital Image Forensics and Counter Anti-forensics

Singh

Joshi

2016

Proceedings of the International Conference on Recent Cognizance in Wireless Communication &Amp; Image Processing

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The use of digital images in journalism and as evidence in the court of law for crime investigation and law enforcement are some application areas where it is extremely important to ensure authenticity of the images being used. Wide availability of sophisticated image processing software has simplified image manipulation. Image forensics deals with the study of the images to reconstruct their history and detect if it has undergone some intentional or unintentional modifications. This paper presents an introductory overview of the digital image forensics and its counterpart aiming to help the beginners understand their alliance so as to develop robust forensic tools against intentional and unintentional attacks on the multimedia. A brief look into counter anti-forensics is also presented in this paper.

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