Distinguishing Computer Graphics from Photographic Images Using Local Binary Patterns

  • Zhaohong Li
  • Jingyu Ye
  • Yun Qing Shi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7809)


With the ongoing development of rendering technology, computer graphics (CG) are sometimes so photorealistic that to distinguish them from photographic images (PG) by human eyes has become difficult. To this end, many methods have been developed for automatic CG and PG classification. In this paper, we explore the statistical difference of uniform gray-scale invariant local binary patterns (LBP) to distinguish CG from PG with the help of support vector machines (SVM). We select YCbCr as the color model. The original JPEG coefficients of Y and Cr components, and their prediction errors are used for LBP calculation. From each 2-D array, we obtain 59 LBP features. In total, four groups of 59 features are obtained from each image. The proposed features have been tested with thousands of CG and PG. Classification accuracy reaches 98.3% with SVM and outperforms the state-of-the-art works.


Image forensics computer graphics local binary patterns image authentication 


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  1. 1.
    Dehnie, S., Sencar, T., Memon, N.: Digital image forensics for identifying computer generated and digital camera images. In: Proc. IEEE ICIP, pp. 2313–2316 (2006)Google Scholar
  2. 2.
    Ng, T.-T., Chang, S.-F., Hsu, J., Xie, L., Tsui, M.-P.: Physics- motivated features for distinguishing photographic images and computer graphics. In: Proc. ACM Multimedia, pp. 239–248 (2005)Google Scholar
  3. 3.
    Dirik, A.E., Bayram, S., Sencar, H.T., Memon, N.: New features to identify computer generated images. In: Proc. IEEE ICIP. IV, pp. 433–436 (2007)Google Scholar
  4. 4.
    Dirik, A.E., Memon, N.: Image tamper detection based on demosaicing artifacts. In: Proc. IEEE ICIP, pp. 1497–1500 (2009)Google Scholar
  5. 5.
    Ng, T.-T., Chang, S.-F.: Classifying Photographic and Photorealistic Computer Graphic Images using Natural Image Statistics. ADVENT Technical Report, Columbia University, #220-2006-6 (2004)Google Scholar
  6. 6.
    Wu, J., Kamath, M.V., Poehlman, W.F.S.: Detecting Differences between Photographs and Computer Generated Images. In: Proceedings of the 24th IASTED International Conference on Signal Processing, Pattern Recognition, and Applications, SPPRA 2006, pp. 268–273 (2006)Google Scholar
  7. 7.
    Chen, W., Shi, Y.Q., Xuan, G.R.: Identifying Computer Graphics Using HSV Color Model and Statistical Moments of Characteristic Functions. In: Proc. ICME, pp. 1123–1126 (2007)Google Scholar
  8. 8.
    Sutthiwan, P., Cai, X., Shi, Y.Q., Zhang, H.: Computer graphics classification based on Markov process model and boosting feature selection technique. In: Proc. IEEE ICIP, pp. 2913–2916 (2009)Google Scholar
  9. 9.
    Chen, D.M., Li, J.H., Wang, S.L., Li, S.H.: Identifying Computer Generated and Digital Camera Images Using Fractional Lower Order Moments. In: IEEE Conference on Industrial Electronics and Applications (ICIEA), pp. 230–235 (2009)Google Scholar
  10. 10.
    Li, W.X., Zhang, T., Zheng, E.G., Ping, X.J.: Identifying Photorealistic Computer Graphics Using Second-order Difference Statistics. In: Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pp. 2316–2319 (2010)Google Scholar
  11. 11.
    Pan, F., Huang, J.: Discriminating computer graphics images and natural images using hidden markov tree model. In: Kim, H.-J., Shi, Y.Q., Barni, M. (eds.) IWDW 2010. LNCS, vol. 6526, pp. 23–28. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Zhang, R., Wang, R.D.: Distinguishing Photorealistic Computer Graphics from Natural Images by Imaging Features and Visual Features. In: International Conference on Electronics, Communications and Control (ICECC), pp. 226–229 (2011)Google Scholar
  13. 13.
    Wu, R.Y., Li, X.L., Yang, B.: Identifying computer generated graphics VIA histogram features. In: Proc. ICIP, pp. 1933–1936 (2011)Google Scholar
  14. 14.
    Haralick, R.M., Dinstein, Shanmugan, K.: Textural features for image classification. IEEE Transaction on Systems, Man and Cybernetics 3(6), 610–621 (1973)CrossRefGoogle Scholar
  15. 15.
    Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 971–987 (2002)CrossRefMATHGoogle Scholar
  16. 16.
    Mäenpää, T., Pietikäinen, M.: Texture analysis with local binary patterns. In: Handbook of Pattern Recognition and Computer Vision, 3rd edn., pp. 197–216 (2005)Google Scholar
  17. 17.
    Chen, W.: Detection of Digital Image and Video Forgeries, Ph.D. Dissertation, Dept. of ECE, NJIT (2008)Google Scholar
  18. 18.
    Weinberger, M.J., Seroussi, G., Sapiro, G.: LOCO-I: a low complexity, context-based, lossless image compression algorithm. In: Proceedings of Data Compression Conference, DCC 1996, pp. 140–149 (1996)Google Scholar
  19. 19.
  20. 20.
    Friedman, J., Hastie, T.: Additive logistic regression: a statistical view of boosting. The Annals of Statistics 28(2), 337–407 (2000)MathSciNetCrossRefMATHGoogle Scholar
  21. 21.
    Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
  22. 22.
    Gloe, T., Böhme, R.: The ’Dresden Image Database’ for benchmarking digital image forensics. Presented at the Proceedings of the 2010 ACM Symposium on Applied Computing, Sierre, Switzerland (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Zhaohong Li
    • 1
    • 2
    • 3
  • Jingyu Ye
    • 1
  • Yun Qing Shi
    • 1
  1. 1.Department of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewarkUSA
  2. 2.School of Electronic and Information EngineeringBeijing Jiaotong UniversityBeijingChina
  3. 3.Shanghai Key Laboratory of Integrate Administration Technologies for Information SecurityShanghaiChina

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