Forensics feature analysis in quaternion wavelet domain for distinguishing photographic images and computer graphics

  • Jinwei Wang
  • Ting Li
  • Yun-Qing Shi
  • Shiguo Lian
  • Jingyu Ye
Article

Abstract

In this paper, a novel set of features based on Quaternion Wavelet Transform (QWT) is proposed for digital image forensics. Compared with Discrete Wavelet Transform (DWT) and Contourlet Wavelet Transform (CWT), QWT produces the parameters, i.e., one magnitude and three angles, which provide more valuable information to distinguish photographic (PG) images and computer generated (CG) images. Some theoretical analysis are done and comparative experiments are made. The corresponding results show that the proposed scheme achieves 18 percents’ improvements on the detection accuracy than Farid’s scheme and 12 percents than Özparlak’s scheme. It may be the first time to introduce QWT to image forensics, but the improvements are encouraging.

Keywords

Quaternion wavelet transform Contourlet wavelet transform Discrete wavelet transform Feature comparison Forensics 

References

  1. 1.
    Buccigrossi R, Simoncelli E (1999) Image compression via joint statistical characterization in the wavelet domain. IEEE Trans Image Process 8(12):1688–701CrossRefGoogle Scholar
  2. 2.
    Bülow T (1999) Hypercomplex spectral signal representations for the processing and analysis of images. Ph.D. dissertation, Christian Albrechts University, Kiel, GermanyGoogle Scholar
  3. 3.
    Chen W, Shi Y, Xuan G (2007) Identifying computer graphics using HSV color model and statistical moments of characteristic functions. In: Proceedings of ICME, pp 1123–1126Google Scholar
  4. 4.
    Delp E, Memon N, Wu M (2009) Digital forensics. IEEE Signal Processing 26(2):14–15CrossRefGoogle Scholar
  5. 5.
    Fan S, Wang R, Zhang Y, Guo K (2012) Classifying computer generated graphics and natural images based on image contour. Int J Inf Comput Sci 9(10):2877–2895Google Scholar
  6. 6.
    Farid H, Lyu S (2003) Higher-order wavelet statistics and their application to digital forensics. In: IEEE workshop on statistical analysis in computer vision, Madison Wisconsin, pp 1–8Google Scholar
  7. 7.
    Friedman J, Hastie T (2000) Additive logistic regression: a statistical view of boosting. Ann Stat 28(2):337–407MathSciNetCrossRefMATHGoogle Scholar
  8. 8.
    Li C, Li J, Fu B (2013) Magnitude-phase of quaternion wavelet transform for texture representation using multilevel copula. IEEE Signal Process Lett 20(8):799–802CrossRefGoogle Scholar
  9. 9.
    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
  10. 10.
    Li Z, Ye J, Shi Y (2012) Distinguishing computer graphics from photographic images using local binary patterns. In: The 11th IWDW, international workshop on digital-forensics and watermarkingGoogle Scholar
  11. 11.
    Liao X, Shu C (2015) Reversible data hiding in encrypted images based on absolute mean difference of multiple neighboring pixels. J Vis Commun Image Represent 28(4):21–27CrossRefGoogle Scholar
  12. 12.
    Liu Y, Jin J, Wang Q, Shen Y (2013) Phases measure of image sharpness based on quaternion wavelet. Pattern Recogn Lett 34:1063–1070CrossRefGoogle Scholar
  13. 13.
    Lyu S, Farid H (2005) How realistic is photorealistic? IEEE Trans Signal Process 53(2):845–850MathSciNetCrossRefGoogle Scholar
  14. 14.
    Ng T, Chang S, Hsu J, Xie L, Tsui M (2005) Physics- motivated features for distinguishing photographic images and computer graphics. In: Proceedings of ACM multi-media, pp 239–248Google Scholar
  15. 15.
    Özparlak L, Avcıbaş I (2011) Differentiating between images using wavelet-based transforms: a comparative study. IEEE Trans Inf Forensics Secur 6(4):1418–1431CrossRefGoogle Scholar
  16. 16.
    Pang H, Zhu M, Guo L (2012) Multifocus color image fusion using quaternion wavelet transform. In: The 5th international congress on image and signal processing, pp 543–546Google Scholar
  17. 17.
    Selesnick I, Baraniuk R, Kingsbury N (2005) The dual-tree complex wavelet transform. IEEE Signal Process 22(6):123–151CrossRefGoogle Scholar
  18. 18.
    Soulard R, Carré P (2010) Quaternionic wavelets for image coding. In: 18th European signal processing conference (EUSIPCO-2010), Aalborg, DenmarkGoogle Scholar
  19. 19.
    Soulard R, Carré P (2011) Quaternionic wavelets for texture classification. Pattern Recogn Lett 32(13):1669–1678CrossRefGoogle Scholar
  20. 20.
    Zhang X (2011) Reversible data hiding in encrypted image. IEEE Signal Process Lett 18(4):255–258CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Jinwei Wang
    • 1
    • 2
  • Ting Li
    • 1
    • 2
  • Yun-Qing Shi
    • 3
  • Shiguo Lian
    • 4
  • Jingyu Ye
    • 3
  1. 1.Jiangsu Engineering Center of Network MonitoringNanjing University of Information Science and TechnologyNanjingChina
  2. 2.School of Computer and SoftwareNanjing University of Information Science and TechnologyNanjingPeople’s Republic of China
  3. 3.New Jersey Institute of TechnologyNewarkUSA
  4. 4.Central Research InstituteHuawei TechnologiesBeijingPeople’s Republic of China

Personalised recommendations