Median filtering forensics in digital images based on frequency-domain features View Full Text


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Article Info

DATE

2017-11

AUTHORS

Anan Liu, Zhengyu Zhao, Chengqian Zhang, Yuting Su

ABSTRACT

Tampering detection has been increasingly attracting attention in the field of digital forensics. As a popular nonlinear smoothing filter, median filtering is often used as a post-processing operation after image forgeries such as copy-paste forgery (including copy-move and image splicing), which is of particular interest to researchers. To implement the blind detection of median filtering, this paper proposes a novel approach based on a frequency-domain feature coined the annular accumulated points (AAP). Experimental results obtained on widely used databases, which consists of various real-world photos, show that the proposed method achieves outstanding performance in distinguishing median-filtered images from original images or images that have undergone other types of manipulations, especially in the scenarios of low resolution and JPEG compression with a low quality factor. Moreover, our approach remains reliable even when the feature dimension decreases to 5, which is significant to save the computing time required for classification, demonstrating its great advantage to be applied in real-time processing of big multimedia data. More... »

PAGES

22119-22132

References to SciGraph publications

  • 2016-03. How important is location information in saliency detection of natural images in MULTIMEDIA TOOLS AND APPLICATIONS
  • 1981. Median Filtering: Statistical Properties in TWO-DIMENSIONAL DIGITAL SIGNAL PRCESSING II
  • 1981. Two-Dimensional Digital Signal Processing I, Linear Filters in NONE
  • 2016-12-30. Smooth filtering identification based on convolutional neural networks in MULTIMEDIA TOOLS AND APPLICATIONS
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11042-017-4845-0

    DOI

    http://dx.doi.org/10.1007/s11042-017-4845-0

    DIMENSIONS

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