An Advanced Texture Analysis Method for Image Sharpening Detection

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9569)

Abstract

Sharpening is a kind of basic yet widely utilized digital image processing techniques designed and utilized to pursue better image quality from human visual point of view. In image forensics it is required to detect this kind of operation. Huge progress has been made in this area in recent years. Overshoot artifact, as a unique phenomenon occurring on image edges after sharpening, is important in sharpening detection. In this paper, an advanced scheme for overshoot artifact determination is proposed to boost the detection performance in the case of mildor overshoot artifact-controlled sharpening, Several groups of experiments have been conducted to corroborate the new scheme possesses the best ability for blind sharpening detection regardless of the strength of overshoot artifact.

Keywords

Image forensics Edge perpendicular binary code Overshoot artifact Sharpening detection 

References

  1. 1.
    Lyu, S., Farid, H.: How realistic is photorealistic? IEEE Trans. Sig. Process. 53(2), 845–850 (2005)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Farid, H.: Digital image forensics. Sci. Am. 298(6), 66–71 (2008)CrossRefGoogle Scholar
  3. 3.
    Fridrich, J.: Digital image forensics. IEEE Sig. Process. Mag. 26(2), 26–37 (2009)CrossRefGoogle Scholar
  4. 4.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Prentice-Hall, Upper Saddle River (2002)Google Scholar
  5. 5.
    Cao, G., Zhao, Y., Ni, R.: Detection of image sharpening based on histogram aberration and ringing artifacts. In: IEEE International Conference on Multimedia and Expo, ICME 2009, pp. 1026–1029. IEEE (2009)Google Scholar
  6. 6.
    Cao, G., Zhao, Y., Ni, R., Kot, A.C.: Unsharp masking sharpening detection via overshoot artifacts analysis. IEEE Sig. Process. Lett. 18(10), 603–606 (2011)CrossRefGoogle Scholar
  7. 7.
    Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)CrossRefGoogle Scholar
  8. 8.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)CrossRefMATHGoogle Scholar
  9. 9.
    Xu, G., Shi, Y.Q.: Camera model identification using local binary patterns. In: 2012 IEEE International Conference on Multimedia and Expo (ICME), pp. 392–397. IEEE (2012)Google Scholar
  10. 10.
    Li, Z., Ye, J., Shi, Y.Q.: Distinguishing computer graphics from photographic images using local binary patterns. In: Shi, Y.Q., Kim, H.-J., Pérez-González, F. (eds.) IWDW 2012. LNCS, vol. 7809, pp. 228–241. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Ding, F., Zhu, G., Shi, Y.: A novel method for detecting image sharpening based on local binary pattern. In: International Workshop on Digital Forensics and Watermaking, IWDW (2013)Google Scholar
  12. 12.
    Ding, F., Zhu, G., Yang, J., Xie, J., Shi, Y.Q.: Edge perpendicular binary coding for usm sharpening detection. IEEE Sig. Process. Lett. 22(3), 327–331 (2015)CrossRefGoogle Scholar
  13. 13.
    Canny, J.F.: Finding edges and lines in images. Massachusetts Institute of Technology Report 1 (1983)Google Scholar
  14. 14.
    Ramponi, G., Strobel, N.K., Mitra, S.K., Yu, T.H.: Nonlinear unsharp masking methods for image contrast enhancement. J. Electron. Imaging 5(3), 353–366 (1996)CrossRefGoogle Scholar
  15. 15.
    Polesel, A., Ramponi, G., Mathews, V.J.: Image enhancement via adaptive unsharp masking. IEEE Trans. Image Process. 9(3), 505–510 (2000)CrossRefGoogle Scholar
  16. 16.
    Lee, J.S.: Digital image enhancement and noise filtering by use of local statistics. IEEE Trans. Pattern Anal. Mach. Intell. 2, 165–168 (1980)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Feng Ding
    • 1
  • Weiqiang Dong
    • 1
  • Guopu Zhu
    • 2
  • Yun-Qing Shi
    • 1
  1. 1.Department of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewarkUSA
  2. 2.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina

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