Blind Forensics of Median Filtering Based on Markov Statistics in Median-Filtered Residual Domain

  • Yujin Zhang
  • Chenglin Zhao
  • Feng Zhao
  • Shenghong Li
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 246)


Revealing the processing history of a digital image has received a great deal of attention from forensic analyzers in recent years. Median filtering is a non-linear operation and has been used widely for noise removal and image enhancement. Therefore, exposing the traces introduced by such operation is helpful to forensic analyzers. In this paper, a passive forensic method to detect median filtering in digital images is proposed. Since overlapped window filtering introduces the correlation among the elements of the median-filtered residual (MFR) which is referred to as the difference between a test image and its corresponding median-filtered version, the transition probability matrices along the horizontal, vertical, main diagonal and minor diagonal directions are calculated from the MFR to characterize the correlation among the elements of the MFR. All elements of these transition probability matrices are served as discriminative features for median filtering detection. Experiment results demonstrate the effectiveness of the proposed method.


Image forensics Median filtering Median-filtered residual 



This work is funded by National Science Foundation of China (61271316, 61071152, and 61271180), 973 Program (2010CB731403, 2010CB731406, and 2013CB329605) of China, Chinese National “Twelfth Five-Year” Plan for Science & Technology Support (2012BAH38 B04), Key Laboratory for Shanghai Integrated Information Security Management Technology Research, and Chinese National Engineering Laboratory for Information Content Analysis Technology. We would like to thank Prof. Yuan for his kindness by providing us with the code of the MFF scheme in [7].


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yujin Zhang
    • 1
  • Chenglin Zhao
    • 2
  • Feng Zhao
    • 3
  • Shenghong Li
    • 1
  1. 1.Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghaiChina
  2. 2.School of Information and Communication EngineeringBeijing University of Posts and TelecommunicationsBeijingChina
  3. 3.Department of Science and TechnologyGuilin University of Electronic TechnologyGuilinChina

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