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

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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.

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Acknowledgments

This work was supported in part by the National Natural Science Foundation of China (61472275, 61572356), the Tianjin Research Program of Application Foundation and Advanced Technology (15JCYBJC16200), a grant from the China Scholarship Council (201506255073), and a grant from the Elite Scholar Program of Tianjin University (2014XRG-0046).

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Authors and Affiliations

  1. School of Electronical and Information Engineering, Tianjin University, Tianjin, China

    Anan Liu, Zhengyu Zhao & Yuting Su

  2. School of Electrical Engineering and Information, Southwest Petroleum University, Chengdu, 610500, China

    Chengqian Zhang

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  1. Anan Liu
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  2. Zhengyu Zhao
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  3. Chengqian Zhang
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  4. Yuting Su
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Correspondence to Yuting Su.

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Liu, A., Zhao, Z., Zhang, C. et al. Median filtering forensics in digital images based on frequency-domain features. Multimed Tools Appl 76, 22119–22132 (2017). https://doi.org/10.1007/s11042-017-4845-0

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  • DOI: https://doi.org/10.1007/s11042-017-4845-0

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