Skip to main content

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

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 246))

Abstract

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.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Cox IJ, Kilian J, Leighton FT, Shamoon T (1997) Secure spread spectrum watermarking for multimedia. IEEE Trans Image Process 6(12):1673–1687

    Article  Google Scholar 

  2. Luo W, Huang J, Qiu G (2010) JPEG error analysis and its applications to digital image forensics. IEEE Trans Inform Forensics Secur 5(3):480–491

    Article  Google Scholar 

  3. Shi YQ, Chen C, Chen W (2007) A natural image model approach to splicing detection. In: Proceedings of ACM Multimedia and Security Workshop, Dallas, TX, 20–21 September, 2007, pp. 51–62

    Google Scholar 

  4. Kirchner M, Böhme RJ (2008) Hiding traces of resampling in digital images. IEEE Trans Inform Forensics Secur 3(4):582–592

    Article  Google Scholar 

  5. Kirchner M, Fridrich J (2010) On detection of median filtering in digital images. Proc SPIE Electron Imag Med Forensics Secur II 7541:1–12

    Google Scholar 

  6. Cao G, Zhao Y, Ni R, Yu L, Tian H (2010) Forensic detection of median filtering in digital images. In: Proceedings of the 2010 I.E. international conference on multimedia and expo, Suntec City, 19–23 July, 2010, pp. 89–94

    Google Scholar 

  7. Yuan HD (2011) Blind forensics of median filtering in digital images. IEEE Trans Inform Forensics Secur 6(4):1335–1345

    Article  Google Scholar 

  8. Chen C, Ni J, Huang R, Huang J (2013) Blind median filtering detection using statistics in difference domain. In: Proceedings of 14th information hiding. LNCS 7692:1–15

    Google Scholar 

  9. Kang X, Stamm MC, Peng A, Liu KJR (2012) Robust median filtering forensics based on the autoregressive model of median filtered residual. In: Proceedings of signal & information processing association annual summit and conference, Hollywood, CA, 3–6 December, 2012, pp. 1–9

  10. Bovik AC (1987) Streaking in median filtered images. IEEE Trans Acous Speech Signal Process 35(4):493–503

    Article  MATH  Google Scholar 

  11. NRCS Photo Gallery [Online]: http://photogallery.nrcs.usda.gov/res/sites/photogallery/

  12. Chang CC, Lin CJ (2001) LIBSVM: a library for support vector machines [Online]. http://www.csie.ntu.edu.tw/cjlin/libsvm

Download references

Acknowledgments

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].

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shenghong Li .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Zhang, Y., Zhao, C., Zhao, F., Li, S. (2014). Blind Forensics of Median Filtering Based on Markov Statistics in Median-Filtered Residual Domain. In: Zhang, B., Mu, J., Wang, W., Liang, Q., Pi, Y. (eds) The Proceedings of the Second International Conference on Communications, Signal Processing, and Systems. Lecture Notes in Electrical Engineering, vol 246. Springer, Cham. https://doi.org/10.1007/978-3-319-00536-2_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-00536-2_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-00535-5

  • Online ISBN: 978-3-319-00536-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics