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Detection of median filtering based on ARMA model and pixel-pair histogram feature of difference image

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Abstract

Median filtering is a widely used method for removing noise and smoothing regions of an image, and the detection of median filtering has drawn much attention from researchers of image forensic. A new robust detection scheme of median filtering based on pixel-pair histogram (PPH) and coefficients of autoregressive moving average model (ARMA) of difference image is proposed in this paper. In the proposed scheme, the PPH and ARMA are extracted from the difference image in four directions; the generated PPH-ARMA feature of 396 dimensions can effectively be used to detect the median filtering. In order to verify the effectiveness of the proposed scheme, a series of experiments on single database and compound databases are conducted, and the experimental results show that, the proposed scheme outperforms many existing algorithms. Moreover, the suggested approach achieves best performance in single dataset and multiple compound datasets compared with state-of-the-art methods, especially for strong JPEG compression and low resolution images.

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References

  1. Bas P, Furon T (2007) Break our watermarking system. http://bows2.gipsa-lab.inpg.fr

  2. Bas P, Filler T, Pevny T (2011) Break our steganographic system: the ins and outs of organizing BOSS. In: Information hiding. Springer, Berlin, pp 59–70

  3. Cao G, Zhao Y, Ni R, Yu L, Tian H (2010) Forensic detection of median filtering in digital images. In: Proceedings IEEE Int. Conf. Multimedia and Expo, pp 89–94

  4. Cao G, Zhao Y, Ni R, Li X (2014) Contrast enhancement-based forensics in digital images. IEEE Trans Inf Forensics Secur 9:515–525

    Article  Google Scholar 

  5. 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 

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

    Article  Google Scholar 

  7. Ding F, Zhu G, Yang J et al (2015) Edge perpendicular binary coding for USM sharpening detection. IEEE Signal Process Lett 22(3):327–331

    Article  Google Scholar 

  8. Ding F, Zhu G, Dong W, Shi Y (2018) An efficient weak sharpening detection method for image forensics. J Vis Commun Image Represent 50:93–99

    Article  Google Scholar 

  9. Gao H, Hu M, Gao T, Cheng R (2018) An effective image detection algorithm for USM sharpening based on pixel-pair histogram. In: Proceedings of the Pacific-Rim Conference on Multimedia, pp 396–407

  10. Gao H, Hu M, Gao T, Cheng R (2019) Robust detection of median filtering based on combined features of difference image. Signal Process Image Commun 72:126–133

    Article  Google Scholar 

  11. Gloe T, Bohme R (2010) Dresden image database for benchmarking digital image forensics. In: Acm symposium on applied computing, pp 1584–1590

  12. He P, Jiang X, Sun T, Wang S (2017) Detection of double compression in MPEG-4 videos based on block artifact measurement. Neurocomputing 228:84–96

    Article  Google Scholar 

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

    Article  Google Scholar 

  14. Kirchner M, Fridrich J (2010) On detection of median filtering in digital images. In: Proceedings SPIE, Electronic Imaging, Media Forensics and Security II, vol 7541, pp 1–12

  15. Lai Y, Gao T, Li J, Sheng G (2015) Forensic detection of median filtering in digital images using the coefficient-pair histogram of DCT value and LBP pattern. In: Proceedings Int. Conf. Intelligent, pp 421–432

  16. Li B, Ng T-T, Li X, Tan S, Huang J (2015) Revealing the trace of high-quality JPEG compression through quantization noise analysis. IEEE Trans Inf Forensics Secur 10(3):558–573

    Article  Google Scholar 

  17. Liu A, Zhao Z, Zhang C, Su Y (2017) Median filtering forensics in digital images based on frequency-domain features. Multimed Tools Appl 76:22119–22132

    Article  Google Scholar 

  18. Mahmood T, Irtaza A, Mehmood Z, Mahmood MT (2017) Copy–move forgery detection through stationary wavelets and local binary pattern variance for forensic analysis in digital images. Forensic Sci Int 279:8–21

    Article  Google Scholar 

  19. Nirenberg L (1971) A proof of the malgrange preparation theorem. Springer Lecture Notes in Math 192:97–105

    Article  MathSciNet  Google Scholar 

  20. Niu Y, Zhao Y, Ni R (2017) Robust median filtering detection based on local difference descriptor. Signal Process Image Commun 53:65–72

    Article  Google Scholar 

  21. Schaefer G, Stich M (2003) UCID -an uncompressed colour image database. Storage & Retrieval Methods & Applications for Multimedia 5307:472–480

    Google Scholar 

  22. Schölkopf B, Smola A, Müller KR (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10(5):1299–1319

    Article  Google Scholar 

  23. Shabanifard M, Shayesteh MG, Akhaee MA (2013) Forensic detection of image manipulation using the Zernike moments and pixel-pair histogram. IET Image Process 7(9):817–828

    Article  Google Scholar 

  24. Shen Z, Ni J, Chen C (2016) Blind detection of median filtering using linear and nonlinear descriptors. Multimed Tools Appl 75:2327–2346

    Article  Google Scholar 

  25. Tang H, Ni R, Zhao Y, Li X (2018) Median filtering detection of small-size image based on CNN. J Vis Commun Image Represent 51:162–168

    Article  Google Scholar 

  26. United States Department of Agriculture (2002) Natural resources conservation service photo gallery. http://photogallery.nrcs.usda.gov/

  27. Vaishali D, Ramesh R, Gomathy C, Anita Christaline J (2017) Histopathology image analysis and classification using ARMA models: application to brain Cancer detection. Current Medical Imaging Reviews 13(3):355–361

    Article  Google Scholar 

  28. Wang D, Gao T, Yang F (2018) A forensic algorithm against median filtering based on coefficients of image blocks in frequency domain. Multimed Tools Appl 4:1–17

    Google Scholar 

  29. Yang J, Xie J, Zhu G, Kwong S, Shi Y (2014) An effective method for detecting double JPEG compression with the same quantization matrix. IEEE Trans Inf Forensics Secur 9(11):1933–1942

    Article  Google Scholar 

  30. Yang L, Gao T, Xuan Y, Gao H (2016) Contrast modification forensics algorithm based on merged weight histogram of run length. International Journal of Digital Crime and Forensics 8(2):27–35

    Article  Google Scholar 

  31. Yang J, Ren H, Zhu G, Huang J, Shi Y (2018) Detecting median filtering via two-dimensional AR models of multiple filtered residuals. Multimed Tools Appl 77:7931–7953

    Article  Google Scholar 

  32. Yao H, Wang S, Zhang X, Qin C, Wang J (2017) Detecting image splicing based on noise level inconsistency. Multimed Tools Appl 76(10):12457–12479

    Article  Google Scholar 

  33. Yao H, Cao F, Tang Z, Wang J, Qiao T (2018) Expose noise level inconsistency incorporating the inhomogeneity scoring strategy. Multimed Tools Appl 77(14):18139–18161

    Article  Google Scholar 

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

    Article  Google Scholar 

  35. Zhang Y, Li S, Wang S, Shi Y (2014) Revealing the traces of median filtering using high-order local ternary patterns. IEEE Signal Process Lett 21(3):275–279

    Article  Google Scholar 

  36. Zhao X, Wang S, Li S, Li J (2015) Passive image-splicing detection by a 2-D Noncausal Markov model. IEEE Trans Circuits Syst Video Technol 25(2):185–199

    Article  Google Scholar 

  37. Zielinski J, Bouaynaya N, Schonfeld D Two-dimensional ARMA modeling for breast cancer detection and classification. In: Proceedings of 2010 International Conference on Signal Processing and Communications (SPCOM)

Download references

Acknowledgements

The authors are grateful to Prof. Guopu Zhu for kindly offering the code of [31] for comparison. The work was supported by the Program of Natural Science Fund of Tianjin, China (Grant NO. 16JCYBJC15700).

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Correspondence to Tiegang Gao.

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Gao, H., Gao, T. Detection of median filtering based on ARMA model and pixel-pair histogram feature of difference image. Multimed Tools Appl 79, 12551–12567 (2020). https://doi.org/10.1007/s11042-019-08340-3

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  • DOI: https://doi.org/10.1007/s11042-019-08340-3

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