Smoothing identification for digital image forensics

  • Feng Ding
  • Yuxi Shi
  • Guopu ZhuEmail author
  • Yun-Qing Shi


With the explosive development in digital techniques, ordinary people without professional training are capable to edit digital images with applications. As a common image processing manipulation, smoothing is important in editing digital images for denoising and producing blur effect. Besides, in recent years, people prefer to retouch images with smoothing algorithms to pursue better appearance. Hence it is required to expose such manipulations in digital image forensics. In this paper, a new scheme for detecting the operation of smoothing is proposed. The proposed scheme is based on analyzing the statistical property which can be considered as computation efficiently when compares to machine learning algorithms. Furthermore, a method for texture analysis is also proposed to specify the algorithm that used for smoothing. The second method adopt the features extracted from edge area. The features are fed into support vector machine for classification.


Image forensics Smoothing detection Bilateral filter Texture analysis Machine learning 



The authors greatly appreciate the anonymous reviewers for their valuable comments. This work was supported in part by the National Natural Science Foundation of China under Grant 61572489 and Grant 61872350, in part by the Basic Research Program of Shenzhen under Grant JCYJ20170818163403748, in part by the Youth Innovation Promotion Association of CAS under Grant 2015299, in part by the CAS Light of West China Program under Grant 2016-QNXZ-A-5, in part by the Science and Technology Planning Project of Guangdong Province under Grant 2017A050501027, and in part by the Shenzhen Discipline Construction Project for Urban Computing and Data Intelligence.


  1. 1.
    Bayar B, Stamm MC (2016) A deep learning approach to universal image manipulation detection using a new convolutional layer. In: Proceedings of the 4th ACM workshop on information hiding and multimedia security. ACM, pp 5–10Google Scholar
  2. 2.
    Canny J (1987) A computational approach to edge detection. In: Readings in computer vision. Elsevier, pp 184–203Google Scholar
  3. 3.
    Cao G, Zhao Y, Ni R (2010) Edge-based blur metric for tamper detection. Journal of Information Hiding and Multimedia Signal Processing 1(1):20–27Google Scholar
  4. 4.
    Cao G, Zhao Y, Ni R, Yu L, Tian H (2010) Forensic detection of median filtering in digital images. In: 2010 IEEE international conference on multimedia and expo (ICME). IEEE, pp 89–94Google Scholar
  5. 5.
    Cao G, Zhao Y, Ni R, Li X (2014) Contrast enhancement-based forensics in digital images. IEEE Trans Inf Forensics Secur 9(3):515–525CrossRefGoogle Scholar
  6. 6.
    Chang T, Kuo C-C (1993) Texture analysis and classification with tree-structured wavelet transform. IEEE Trans Image Process 2(4):429–441CrossRefGoogle Scholar
  7. 7.
    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):4699–4710MathSciNetCrossRefGoogle Scholar
  8. 8.
    Ding F, Zhu G, Yang J, Xie J, Shi Y-Q (2015) Edge perpendicular binary coding for usm sharpening detection. IEEE Signal Process Lett 22(3):327–331CrossRefGoogle Scholar
  9. 9.
    Ding F, Zhu G, Dong W, Shi Y-Q (2018) An efficient weak sharpening detection method for image forensics. J Vis Commun Image Represent 50:93–99CrossRefGoogle Scholar
  10. 10.
    Drucker H, Wu D, Vapnik VN (1999) Support vector machines for spam categorization. IEEE Trans Neural Netw 10(5):1048–1054CrossRefGoogle Scholar
  11. 11.
    Elad M (2002) On the origin of the bilateral filter and ways to improve it. IEEE Trans Image Process 11(10):1141–1151MathSciNetCrossRefGoogle Scholar
  12. 12.
    Farid H (2008) Digital image forensics. Sci Am 298(6):66–71CrossRefGoogle Scholar
  13. 13.
    Fridrich J (2004) Feature-based steganalysis for jpeg images and its implications for future design of steganographic schemes. In: International workshop on information hiding. Springer, pp 67–81Google Scholar
  14. 14.
    Fridrich J (2009) Digital image forensics. IEEE Signal Process Mag 26(2):26–37CrossRefGoogle Scholar
  15. 15.
    Fridrich J, Soukal D, Lukáš J (2003) Detection of copy-move forgery in digital images. In: Proceedings of digital forensic research workshop. CiteseerGoogle Scholar
  16. 16.
    Gonzalez RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice-Hall, Upper Saddle RiverGoogle Scholar
  17. 17.
    Guo L, Ni J, Shi YQ (2014) Uniform embedding for efficient jpeg steganography. IEEE Trans Inf Forensics Secur 9(5):814–825CrossRefGoogle Scholar
  18. 18.
    Haddock LJ, Kim DY, Mukai S (2013) Simple, inexpensive technique for high-quality smartphone fundus photography in human and animal eyes. J Ophthalmol 2013:5Google Scholar
  19. 19.
    Hu R, Li X, Guo Z (2018) Decorrelated local binary patterns for efficient texture classification. Multimed Tools Appl 77(6):6863–6882CrossRefGoogle Scholar
  20. 20.
    Idaho N, et al. (2012) The plants database. In: NRCS, national plant data team. CiteseerGoogle Scholar
  21. 21.
    Kang X, Stamm MC, Peng A, Liu KR (2013) Robust median filtering forensics using an autoregressive model. IEEE Trans Inf Forensics Secur 8(9):1456–1468CrossRefGoogle Scholar
  22. 22.
    Kirchner M, Fridrich J (2010) On detection of median filtering in digital images. In: Media forensics and security II, vol 7541. International society for optics and photonics, p 754110Google Scholar
  23. 23.
    Kuan DT, Sawchuk AA, Strand TC, Chavel P (1985) Adaptive noise smoothing filter for images with signal-dependent noise. IEEE Trans Pattern Anal Mach Intell 2:165–177CrossRefGoogle Scholar
  24. 24.
    Lee J-S (1983) Digital image smoothing and the sigma filter. Comput Vis, Graphics, Image Process 24(2):255–269CrossRefGoogle Scholar
  25. 25.
    Li B, He J, Huang J, Shi Y-Q (2011) A survey on image steganography and steganalysis. Journal of Information Hiding and Multimedia Signal Processing 2 (2):142–172Google Scholar
  26. 26.
    Li J, Li X, Yang B, Sun X (2015) Segmentation-based image copy-move forgery detection scheme. IEEE Trans Inf Forensics Secur 10(3):507–518CrossRefGoogle Scholar
  27. 27.
    Lukas J, Fridrich J, Goljan M (2006) Digital camera identification from sensor pattern noise. IEEE Trans Inf Forensics Secur 1(2):205–214CrossRefGoogle Scholar
  28. 28.
    Luo X, Song X, Li X, Zhang W, Lu J, Yang C, Liu F (2016) Steganalysis of hugo steganography based on parameter recognition of syndrome-trellis-codes. Multimed Tools and Appl 75(21):13557– 13583CrossRefGoogle Scholar
  29. 29.
    Ma Y, Luo X, Li X, Bao Z, Zhang Y Selection of rich model steganalysis features based on decision rough set α-positive region reduction, IEEE Transactions on Circuits and Systems for Video Technology, In press.
  30. 30.
    Paris S, Durand F (2006) A fast approximation of the bilateral filter using a signal processing approach. In: European conference on computer vision. Springer, pp 568–580Google Scholar
  31. 31.
    Pasquini C, Boato G, Perez-Gonzalez F (2014) Multiple jpeg compression detection by means of benford-fourier coefficients. In: 2014 IEEE international workshop on information forensics and security (WIFS). IEEE, pp 113–118Google Scholar
  32. 32.
    Schaefer G, Stich M (2003) Ucid: an uncompressed color image database. In: Storage and retrieval methods and applications for multimedia 2004, vol 5307, International Society for Optics and Photonics, pp 472–481Google Scholar
  33. 33.
    Shi J, Xu L, Jia J (2014) Discriminative blur detection features. In: 2014 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, pp 2965–2972Google Scholar
  34. 34.
    Stamm MC, Wu M, Liu KR (2013) Information forensics: An overview of the first decade. IEEE Access 1:167–200CrossRefGoogle Scholar
  35. 35.
    Tang H, Ni R, Zhao Y, Li X (2018) Median filtering detection of small-size image based on cnn. J Vis Commun Image Represent 51:162–168CrossRefGoogle Scholar
  36. 36.
    Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In 1998 6th international conference on computer vision. IEEE, pp 839–846Google Scholar
  37. 37.
    Tuceryan M, Jain AK (1993) Texture analysis. In Handbook of pattern recognition and computer vision. World Scientific, pp 235–276Google Scholar
  38. 38.
    Wang J, Li T, Shi Y-Q, Lian S, Ye J (2017) Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics. Multimed Tools Appl 76(22):23721– 23737CrossRefGoogle Scholar
  39. 39.
    Wang P, Liu F, Yang C, Luo X (2018) Blind forensics of image gamma transformation and its application in splicing detection. Journal of Visual Communication and Image RepresentationGoogle Scholar
  40. 40.
    Wang P, Liu F, Yang C, Luo X (2018) Parameter estimation of image gamma transformation based on zero-value histogram bin locations. Signal Process Image Commun 64:33–45CrossRefGoogle Scholar
  41. 41.
    Wu R, Li X, Yang B (2011) Identifying computer generated graphics via histogram features. In: 2011 18th IEEE international conference on image processing (ICIP). IEEE, pp 1933–1936Google Scholar
  42. 42.
    Wu Y, Li X, Zhao Y, Ni R (2017) A new detector for jpeg decompressed bitmap identification. In: Asia-pacific signal and information processing association annual summit and conference (APSIPA ASC). IEEE, pp 842–845Google Scholar
  43. 43.
    Yang J, Xie J, Zhu G, Kwong S, Shi Y-Q (2014) An effective method for detecting double jpeg compression with the same quantization matrix. IEEE Trans Inf Forensics Secur 9(11):1933– 1942CrossRefGoogle Scholar
  44. 44.
    Yuan H-D (2011) Blind forensics of median filtering in digital images. IEEE Trans Inf Forensics Secur 6(4):1335–1345CrossRefGoogle Scholar
  45. 45.
    Zhang Y, Qin C, Zhang W, Liu F, Luo X (2018) On the fault-tolerant performance for a class of robust image steganography. Signal Process 146:99–111CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewarkUSA
  2. 2.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
  3. 3.State Key Laboratory of Information Security, Institute of Information EngineeringChinese Academy of SciencesBeijingChina

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