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Detecting median filtering via two-dimensional AR models of multiple filtered residuals

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Abstract

Median filtering, being an order statistic filtering, has been widely used in image denoising and recently also in image anti-forensics and anti-steganalysis. In the past few years, several methods have been developed for median filtering detection. However, it is still a challenging task to detect median filtering in JPEG compressed images. In this paper, we propose a novel method to solve this challenging task. We first generate median filtered residual (MFR), average filtered residual (AFR) and Gaussian filtered residual (GFR) by calculating the differences between an original image and its filtered images. Then, we propose to use two-dimensional autoregressive (2D-AR) model to characterize MFR, AFR and GFR separately, and further combine the 2D-AR coefficients of these three residuals into a set of features. Finally, the extracted feature set is fed into a support vector machine classifier for training and detection. Extensive experiments have demonstrated that compared with existing methods, the proposed one can achieve a considerable improvement in detecting median filtering in heavily compressed images.

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Notes

  1. 1 Streaking artifacts, first analyzed by Bovik [2], refer to the phenomenon that the probability of two adjacent pixels being the same increases greatly after median filtering, which can thus be evaluated by using first-order difference images.

References

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

    Chapter  Google Scholar 

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

    Article  MATH  Google Scholar 

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

    Article  Google Scholar 

  4. 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  MATH  Google Scholar 

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

    Google Scholar 

  6. Dang-Nguyen D-T, Gebru ID, Conotter V, Boato G, De Natale FGB (2013) Counter-forensics of median filtering Proceedings IEEE Int. Workshop on Multimedia Signal Processing, pp 260–265

    Google Scholar 

  7. Dirik AE, Memon N (2009) Image tamper detection based on demosaicing artifacts Proceedings IEEE Int. Conf. Image Processing, pp 1497–1500

    Google Scholar 

  8. Dang-Nguyen D-T, Pasquini C, Conotter V, Boato G (2015) RAISE: a raw images dataset for digital image forensics Proceedings 6th ACM Multimedia Systems Conference, pp 219–224

    Google Scholar 

  9. Fan Z, de Queiroz RL (2003) Identification of bitmap compression history: JPEG detection and quantizer estimation. IEEE Trans Image Process 12(2):230–235

  10. Fan W, Wang K, Cayre F, Xiong Z (2015) Median filtered image quality enhancement and anti-forensics via variational deconvolution. IEEE Trans Inf Forensics Secur 10(5):1076–1091

    Article  Google Scholar 

  11. Farid H (2009) Image forgery detection. IEEE Signal Proc Mag 26:16–25

    Article  Google Scholar 

  12. Fontani M, Barni M (2012) Image tamper detection based on demosaicing artifacts Proceedings 20th European Signal Processing Conference, pp 1239–1243

    Google Scholar 

  13. Fridrich J, Kodovsky J (2012) Rich models for steganalysis of digital images. IEEE Trans Inf Forensics Secur 7(3):868–882

    Article  Google Scholar 

  14. Fu D, Shi YQ, Su W (2007) A generalized Benford’s law for JPEG coefficients and its applications in image forensics Proceedings SPIE, Electronic Imaging, Security and Watermarking of Multimedia Contents IX, vol 6505, pp 1L1–1L11

  15. Furon T, Bas P (2008) Broken arrows. EURASIP J Inf Secur 2008, p 3

  16. Gloe T, Böhme R The Dresden Image Database for benchmarking digital image forensics Proceedings 25th Symposium On Applied Computing (ACM SAC 2010), vol 2, pp 1585–1591

  17. Gonzalez RC, Woods RE (2002) Digital image processing. Upper Saddle River NJ: Prentice-hall

  18. Kang X, Li Y, Qu Z, Huang J (2012) Enhancing source camera identification performance with a camera reference phase sensor pattern noise. IEEE Trans Inf Forensics Secur 7(2):393–402

    Article  Google Scholar 

  19. 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 

  20. Kashyap RL (1984) Characterization and estimation of two-dimensional arma models. IEEE Trans Inf Theory 30(25):736–745

    Article  MathSciNet  MATH  Google Scholar 

  21. Kee E, Farid H (2011) A perceptual metric for photo retouching. Proc Natl Acad Sci 108(50):19907–19912

    Article  Google Scholar 

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

    Article  Google Scholar 

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

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

    Google Scholar 

  25. Lin Z, He J, Tang X, Tang C-K (2009) Fast, automatic and fine-grained tampered JPEG image detection via DCT coefficient analysis. Pattern Recogn 42 (11):2492–2501

    Article  MATH  Google Scholar 

  26. Lukas J, Fridrich J, Goljan M (2006) Digital camera identification from sensor pattern noise. IEEE Trans Inf Forensics Secur 1(2):205–214

    Article  Google Scholar 

  27. (2002). Natural resources conservation service photo gallery, http://photogallery.nrcs.usda.gov/, United States Department of Agriculture

  28. Pasquini C, Boato G, Alajlan N, De Natale FGB (2016) A deterministic approach to detect median filtering in 1D data. IEEE Trans Inf Forensics Secur 11 (7):1425–1437

    Article  Google Scholar 

  29. Pevný T, Bas P, Fridrich J (2010) Steganalysis by subtractive pixel adjacency matrix. IEEE Trans Inf Forensics Secur 5(2):215–224

    Article  Google Scholar 

  30. Piva A (2013) An overview on image forensics, ISRN Signal Processing, vol. 2013

  31. Popescu AC, Farid H (2004) Exposing digital forgeries by detecting duplicated image regions, Dept Comput Sci, Dartmouth College, Tech Rep TR2004-515

  32. Qiu X, Li H, Luo W, Huang J (2014) A universal image forensic strategy based on steganalytic model Proceedings 2nd ACM Workshop on Information hiding and multimedia security, pp 165–170

    Google Scholar 

  33. Rocha A, Scheirer W, Boult T, Goldenstein S (2011) Vision of the unseen: Current trends and challenges in digital image and video forensics. ACM Comput Surv 43(4):Article 26

    Article  Google Scholar 

  34. Schaefer G, Stich M (2004) UCID: An Uncompressed color image database Proceedings SPIE 5307, Storage and Retrieval Methods and Applications for Multimedia, vol 5307, pp 472–480

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

    Article  Google Scholar 

  36. Wu Z-H, Stamm MC, Liu KJR (2013) Anti-forensics of median filtering Proceedings IEEE Int. Conf. acoustics, Speech, Signal Process, pp 3043–3047

    Google Scholar 

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

    Article  Google Scholar 

  38. Yang J, Xie J, Zhu G, Kwong S, Shi YQ (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 

  39. 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):275–279

    Article  Google Scholar 

  40. Zeng H, Qin T, Kang X, Liu L (2014) Countering anti-forensics of median filtering Proceedings IEEE Int Conf acoustics, Speech, Signal Process, pp 2704–2708

    Google Scholar 

Download references

Acknowledgments

The authors appreciate the anonymous reviewers for their constructive comments. This work was supported in part by the National Natural Science Foundation of China under Grant 61572489 and Grant 61672554, and in part by the Youth Innovation Promotion Association of CAS under Grant 2015299.

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    Authors and Affiliations

    1. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, GD, 518055, China

      Jianquan Yang, Honglei Ren & Guopu Zhu

    2. Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, GD, 518055, China

      Jianquan Yang

    3. State Key Laboratory of Information Security, Institute of Information Engineering, Chinese Academy of Sciences, Beijing, 100093, China

      Guopu Zhu

    4. College of Information Engineering, Shenzhen University, Shenzhen, GD, 518055, China

      Jiwu Huang

    5. Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA

      Yun-Qing Shi

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    1. Jianquan Yang
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    Correspondence to Guopu Zhu.

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    Jianquan Yang and Honglei Ren contributed equally to this work

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    Yang, J., Ren, H., Zhu, G. et al. Detecting median filtering via two-dimensional AR models of multiple filtered residuals. Multimed Tools Appl 77, 7931–7953 (2018). https://doi.org/10.1007/s11042-017-4691-0

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    • DOI: https://doi.org/10.1007/s11042-017-4691-0

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