Multimedia Tools and Applications

, Volume 76, Issue 15, pp 16563–16580 | Cite as

Image sharpening detection based on multiresolution overshoot artifact analysis

Article
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Abstract

With the wide use of sophisticated photo editing tools, digital image manipulation becomes very convenient, which makes the detection of image tampering significant. Image sharpening, which aims to enhance the contrast of edges in an image, is a ubiquitous image tampering operation. The detection of image sharpening can serve as a reliable clue for image forgery. In this paper, we propose a novel image sharpening detection method based on multiresolution overshoot artifact analysis (MOAA). By building the relationship between the overshoot artifact strength and the slope of a sharpened edge, we find that although undergoing the same sharpening operation, the edge with large slope will present a stronger overshoot artifact than the one with small slope. Based on this finding, we use the nonsubsampled contourlet transform (NSCT) to classify the image edge points into three categories, i.e., weak, middle and strong edge points and measure the overshoot artifact of each category respectively. A cascaded decision strategy is adopted to decide an image is sharpened or not. Experimental results on digital images with various sharpening operators demonstrate the superiority of our proposed method when compared with state-of-the-art approaches.

Keywords

Image forensics Image sharpening detection Nonsubsampled contourlet transform Overshoot artifacts 

Notes

Acknowledgements

The authors would like to thank the Editor-in-Chief, the handling associate editor and all anonymous reviewers for their considerations and suggestions. This work was supported by the National High Technology Research and Development Program of China (2013AA01A602), the National Natural Science Foundation of China (Grant Nos. 61432014 and 61572388) and Program for Changjiang Scholars and Innovative Research Team in University (No. IRT13088).

References

  1. 1.
    Arbelaez P, Maire M, Fowlkes C, Malik J (2011) Contour detection and hierarchical image segmentation. IEEE Trans Pattern Anal Mach Intell 33(5):898–916CrossRefGoogle Scholar
  2. 2.
    Bahrami K, Kot AC, Li L, Li H (2015) Blurred image splicing localization by exposing blur type inconsistency. IEEE Trans Inf Forensic Secur 10(5):999–1009CrossRefGoogle Scholar
  3. 3.
    Bas P, Furon T (2007) Break our watermarking system [online]. Available: http://bows2.gipsa-lab.inpg.fr
  4. 4.
    Bianchi T, Piva A (2012) Detection of non-aligned double jpeg compression based on integer periodicity maps. IEEE Trans Inf Forensic Secur 7(2):842–848CrossRefGoogle Scholar
  5. 5.
    Birajdar GK, Mankar VH (2013) Digital image forgery detection using passive techniques: a survey. Digit Investig 10(3):226–245CrossRefGoogle Scholar
  6. 6.
    Cao G, Zhao Y, Ni R (2009) Detection of image sharpening based on histogram aberration and ringing artifacts. In: Proc. IEEE International Conference on Multimedia and Expo, pp. 1026–1029Google Scholar
  7. 7.
    Cao G, Zhao Y, Ni R, Kot AC (2011) Unsharp masking sharpening detection via overshoot artifacts analysis. IEEE Signal Processing Letters 18(10):603–606CrossRefGoogle Scholar
  8. 8.
    Cao G, Zhao Y, Ni R, Li X (2014) Contrast enhancement-based forensics in digital images. IEEE Trans Inf Forensic Secur 9(3):515–525CrossRefGoogle Scholar
  9. 9.
    Chen M, Fridrich J, Goljan M, Lukas J (2008) Determining image origin and integrity using sensor noise. IEEE Trans Inf Forensic Secur 3(1):74–90CrossRefGoogle Scholar
  10. 10.
    Choi CH, Lee HY, Lee HK (2013) Estimation of color modification in digital images by CFA pattern change. Forensic Sci Int 226(1):94–105CrossRefGoogle Scholar
  11. 11.
    Da Cunha AL, Zhou J, Do MN (2006) The nonsubsampled contourlet transform: theory, design, and applications. IEEE Trans Image Process 15(10):3089–3101CrossRefGoogle Scholar
  12. 12.
    Deng C, Gao X, Li X, Tao D (2010) Local histogram based geometric invariant image watermarking. Signal Process 90(12):3256–3264Google Scholar
  13. 13.
    Ding F, Zhu G, Shi YQ (2013) A novel method for detecting image sharpening based on local binary pattern. In: Proc. International Workshop on Digital-Forensics and Watermarking, pp. 180–191Google Scholar
  14. 14.
    Fan S, Wang R, Zhang Y, Guo K (2012) Classifying computer generated graphics and natural imaged based on image contour information. Int J Inf Comput Sci 9(10):2877–2895Google Scholar
  15. 15.
    Ferrara P, Bianchi T, De Rosa A, Piva A (2012) Image forgery localization via fine-grained analysis of CFA artifacts. IEEE Trans Inf Forensic Secur 7(5):1566–1577CrossRefGoogle Scholar
  16. 16.
    Gao X, Deng C, Li X, Tao D (2010) Geometric distortion insensitive image watermarking in affine covariant regions. IEEE Trans Systems, Man, and Cybernetics, Part C 40(3):278–286Google Scholar
  17. 17.
    Gao L, Song J, Nie F, Zou F, Sebe N, Shen HT (2016). Graph-without-cut: An Ideal Graph Learning for Image Segmentation. In: Proc. AAAI Conference on Artificial Intelligence, pp. 1188–1194Google Scholar
  18. 18.
    Gloe T, Borowka K, Winkler A (2010) Efficient estimation and large-scale evaluation of lateral chromatic aberration for digital image forensics. In: Proc. SPIE Conference on Media Forensics and Security, p. 754107Google Scholar
  19. 19.
    Hou X, Zhang T, Xiong G, Zhang Y, Ping X (2014) Image resampling detection based on texture classification. Multimed Tools Appl 72(2):1681–1708CrossRefGoogle Scholar
  20. 20.
    Hsu YF, Chang SF (2010) Camera response functions for image forensics: an automatic algorithm for splicing detection. IEEE Trans Inf Forensic Secur 5(4):816–825MathSciNetCrossRefGoogle Scholar
  21. 21.
    Johnson MK, Farid H (2007) Exposing digital forgeries in complex lighting environments. IEEE Trans Inf Forensic Secur 2(3):450–461CrossRefGoogle Scholar
  22. 22.
    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 Forensic Secur 7(2):393–402CrossRefGoogle Scholar
  23. 23.
    Liu Q, Cao X, Deng C, Guo X (2011) Identifying image composite through shadow matte consistency. IEEE Trans Inf Forensic Secur 6(3):1111–1122CrossRefGoogle Scholar
  24. 24.
    Liu G, Wang J, Lian S, Dai Y (2013) Detect image splicing with artificial blurred boundary. Math Comput Model 57(11):2647–2659CrossRefMATHGoogle Scholar
  25. 25.
    Lu L, Yang G, Xia M (2013) Anti-forensics for unsharp masking sharpening in digital image. Int J Digital Crime Forensics 5(3):53–65CrossRefGoogle Scholar
  26. 26.
    Mahdian B, Saic S (2008) Blind authentication using periodic properties of interpolation. IEEE Trans Inf Forensic Secur 3(3):529–538CrossRefGoogle Scholar
  27. 27.
    Muammar H, Dragotti PL (2013) An investigation into aliasing in images recaptured from an LCD monitor using a digital camera. In: Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 2242–2246Google Scholar
  28. 28.
    Natural resource conservation service photo gallery [online]. Available: http://photogallery.nrcs.usda.gov/res/sites/PhotoGallery/index.html.
  29. 29.
    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):971–987CrossRefMATHGoogle Scholar
  30. 30.
    Redi JA, Taktak W, Dugelay JL (2011) Digital image forensics: a booklet for beginners. Multimed Tools Appl 51(1):133–162CrossRefGoogle Scholar
  31. 31.
    Schaefer G, Stich M (2004) UCID: an uncompressed color image database. In: Proc. SPIE, Storage and Retrieval Methods and Applications for Multimedia, pp. 472–480Google Scholar
  32. 32.
    Shen Z, Ni J, Chen C (2016) Blind detection of median filtering using linear and nonlinear descriptors. Multimed Tools Appl 75(4):2327–2346CrossRefGoogle Scholar
  33. 33.
    Stamm MC, Liu KJR (2010) Forensic detection of image manipulation using statistical intrinsic fingerprints. IEEE Trans Inf Forensic Secur 5(3):492–506CrossRefGoogle Scholar
  34. 34.
    Thongkamwitoon T, Muammar H, Dragotti PL (2015) An image recapture detection algorithm based on learning dictionaries of edge profiles. IEEE Trans Inf Forensic Secur 10(5):953–968CrossRefGoogle Scholar
  35. 35.
    Wang X, Liu Y, Xu B, Li L, Xue J (2014) A statistical feature based approach to distinguish PRCG from photographs. Comput Vis Image Underst 128:84–93CrossRefGoogle Scholar
  36. 36.
    Zhang R, Wang RD (2015) In-camera jpeg compression detection for doubly compressed images. Multimed Tools Appl 74(15):5557–5575CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.School of Electronic EngineeringXidian UniversityXi’anChina

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