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Median filtering detection using LBP encoding pattern★

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Abstract

In recent years, median filtering detection has a widely application in many fields such as images’ processing history tracking, image editing detection, image anti-forensics analyzing and anti-steganalysis analyzing. In this paper, we propose two median filtering detection algorithms. The Algorithm I is a recognition algorithm that can identify whether a given image has undergone median filtering. The Algorithm II is a discriminating algorithm that can distinguish a median (average, Gaussian) filtered image from unfiltered images. Differing from the general framework of existing median filtering detectors, the contribution of our work is that the presented methods are not based on the statistical learning model. The proposed methods do not need any classifier, or any threshold. These methods are implemented by counting the number of specific Local Binary Pattern encoding patterns of a single image. Experimental results demonstrate that the proposed methods provide high accuracy and broad-spectrum robustness for tolerating content-preserving manipulations. Compared to state-of-the-art methods, the proposed methods exhibit high efficiency, high accuracy, and strong robustness.

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References

  1. Bas P, Furon T (2007) Break Our Watermarking System, [Online]. Available: https://bows2.gipsa-lab.inpg.fr, 2nd ed

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

    Article  Google Scholar 

  3. Cancelli G, Doërr G, Barni M, Cox IJ (2008) A comparative study of 1 steganalyzers in Proc ACM Multimed Secur Workshop, 2008, pp.791–796

  4. Cao G, Zhao Y, Ni R, Yu L, Tian H (2010) Forensic detection of median filtering in digital images, 2010 IEEE International Conference on Multimedia and Expo (ICME), Singapore

  5. Chen J, Kang X, Liu Y, Jane Wang Z (2015) Median filtering forensics based on convolutional neural networks. IEEE Signal Process Lett 22(11):1849–1853

    Article  Google Scholar 

  6. Chen C, Ni J, Huang J (2013) Blind Detection of Median Filtering in Digital Images: A Difference Domain Based Approach. IEEE Trans Image Process 22(12):4699–4710

    Article  MathSciNet  Google Scholar 

  7. Chen YS, Wang RZ (2009) Steganalysis of reversible contrast mapping watermarking. IEEE Signal Process Lett 16(2):125–128

    Article  Google Scholar 

  8. Chuang WH, Swaminathan A, Mu M (2009) Tampering identification using empirical frequency response, in Proc IEEE Int Conf Acoust, Speech, Signal Process, pp. 1517–1520

  9. Farid H (2009) Exposing digital forgeries from JPEG ghosts. IEEE Trans Inf Forensics Secur 4(1):154–160

    Article  MathSciNet  Google Scholar 

  10. Gao S, Liao X, Liu X (2019) Real-time detecting one specific tampering operation in multiple operator chains. J Real-Time Image Proc 16(3):741–750

    Article  Google Scholar 

  11. Heikkilä M, Pietikäinen M (2006) A texture-based method for modeling the background and detecting moving objects. IEEE Trans Pattern Anal Mach Intell 28(4):657–662

    Article  Google Scholar 

  12. Kang X, Stamm MC, Peng A, Liu KJR (2013) Robust median filtering forensics using an autoregressive model. IEEE Trans Inf Forens Sec 8(9):1456–1468

    Article  Google Scholar 

  13. Ker A (2005) Steganalysis of LSB matching in grayscale images. IEEE Signal Process Lett 12(6):441–444

    Article  Google Scholar 

  14. Ker A, Böhme R (2008) Revisiting weighted stego-image steganalysis in Proc SPIE, Security, Forensics, Steganography, and Watermarking of Multimedia Contents, Vol.6819, pp.501–517

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

    Article  Google Scholar 

  16. Kirchner M, Fridrich J (2010) On detection of median filtering in digital images in Electron Imaging, Media Forensics and Security II, vol. 7541 of Proc SPIE, pp.1–12

  17. X. Liao, K. Li, X. Zhu, K.J.R. Liu (2020) Robust detection of image operator chain with two-stream convolutional neural network. IEEE J Sel Top Sign Proces 14(5):955–968

  18. Luo WQ, Huang JW, Qiu GP (2010) JPEG error analysis and its applications to digital image forensics. IEEE Trans Inf Forensics Secur 5(3):480–491

    Article  Google Scholar 

  19. Luo WQ, Wang YG, Huang JW (2010) Detection of quantization artifacts and its applications to transform encoder identification. IEEE Trans Inf Forensics Secur 5(4):810–815

    Article  Google Scholar 

  20. Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987

    Article  Google Scholar 

  21. Pasquini C, Boato G, Alajlan N, de Natale FGB (2016) A Deterministic Approach to Detect Median Filtering in 1D Data. IEEE Trans Inf Forens Sec 11(7):1425–1437

    Article  Google Scholar 

  22. Pevn’Y T, Bas P, Fridrich J (2010) Steganalysis by subtractive pixel adjacency matrix. IEEE Trans Inf Forensics Secur 5(2):215–224

    Article  Google Scholar 

  23. Pevý T, Filler T, Bas P (2010) Using high-dimensional image models to perform highly undetectable steganography in Proc 12th Information Hiding, Jun 28–30, LNCS6387, pp.161–177

  24. Schaefer G, Stich M (2004) UCID-An uncompressed colour image database in Proc. SPIE, Storage and Retrieval Methods and Applications for Multimedia, pp. 472–480.

  25. Shen Z, Ni J, Chen C (2014) Blind detection of median filtering using linear and nonlinear descriptors. Multimed Tools Appl, doi https://doi.org/10.1007/s11042-014-2407-2.

  26. Stamm MC, Liu KJR (2010) Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Trans Inf Forensics Secur 5(3):492–506

    Article  Google Scholar 

  27. Stamm MC, Liu KJR (2011) Anti-forensics of digital image compression. IEEE Trans Inform Forensics Secur 6(3):1050–1065

    Article  Google Scholar 

  28. Swaminathan A, Wu M, Liu KJR (2008) Digital image forensics via intrinsic fingerprints. IEEE Trans Inf Forensics Secur 3(1):101–117

    Article  Google Scholar 

  29. United States Department of Agriculture, “Natural Resources Conservation Service Photo Gallery 2002” [Online]. Available: http://photogallery.nrcs.usda.gov

  30. Yu X, Babaguchi N (2008) Breaking the YASS algorithm via pixel and DCT coefficients analysis, in Proc. 19th IEEE Int. Conf. Pattern Recogn, pp. 1–4.

  31. Yuan H (2011) Blind forensics of median filtering in digital images. IEEE Trans Inf Forensics Secur 6(4):1335–1345

    Article  Google Scholar 

  32. Zhang Y, Li S, Wang S, Shi YQ (2014) Revealing the traces of median filtering using high-order local ternary patterns. IEEE Signal Process Lett 21(3):275–280

    Article  Google Scholar 

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Funding

This work was supported by the National Natural Science Foundation of China, No.61772416 and No.61973094; the Key Laboratory Project of the Education Department of Shaanxi Province, No.17JS098; Shaanxi province technology innovation guiding fund project, No.2018XNCG-G-G-02.

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Authors and Affiliations

  1. School of Science, Xi’an University of Technology, Xi’an, Shaanxi, 710048, China

    Xiaofeng Wang, Xinai Li & Chao Tang

  2. Guangdong University of Petrochemical Technology, Maoming, Guangdong, China

    Shaolin Hu

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  1. Xiaofeng Wang
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  2. Xinai Li
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  3. Chao Tang
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  4. Shaolin Hu
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Correspondence to Xiaofeng Wang.

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Wang, X., Li, X., Tang, C. et al. Median filtering detection using LBP encoding pattern★. Multimed Tools Appl 80, 17721–17744 (2021). https://doi.org/10.1007/s11042-021-10581-0

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  • DOI: https://doi.org/10.1007/s11042-021-10581-0

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