An Enhanced Statistical Approach to Identifying Photorealistic Images

  • Patchara Sutthiwan
  • Jingyu Ye
  • Yun Q. Shi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5703)

Abstract

Computer graphics identification has gained importance in digital era as it relates to image forgery detection and enhancement of high photorealistic rendering software. In this paper, statistical moments of 1-D and 2-D characteristic functions are employed to derive image features that can well capture the statistical differences between computer graphics and photographic images. YCbCr color system is selected because it has shown better performance in computer graphics classification than RGB color system and it has been adopted by the most popularly used JPEG images. Furthermore, only Y and Cb color channels are used in feature extraction due to our study showing features derived from Cb and Cr are so highly correlated that no need to use features extracted from both Cb and Cr components, which substantially reduces computational complexity. Concretely, in each selected color component, features are extracted from each image in both image pixel 2-D array and JPEG 2-D array (an 2-D array consisting of the magnitude of JPEG coefficients), their prediction-error 2-D arrays, and all of their three-level wavelet subbands, referred to as various 2-D arrays generated from a given image in this paper. The rationale behind using prediction-error image is to reduce the influence caused by image content. To generate image features from 1-D characteristic functions, the various 2-D arrays of a given image are the inputs, yielding 156 features in total. For the feature generated from 2-D characteristic functions, only JPEG 2-D array and its prediction-error 2-D array are the inputs, one-unit-apart 2-D histograms of the JPEG 2-D array along the horizontal, vertical and diagonal directions are utilized to generate 2-D characteristic functions, from which the marginal moments are generated to form 234 features. Together, the process then results in 390 features per color channel, and 780 features in total Finally, Boosting Feature Selection (BFS) is used to greatly reduce the dimensionality of features while boosts the machine learning based classification performance to fairly high.

Keywords

Moments of Characteristic Functions Computer graphics classification boosting 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ianeva, T., de Vries, A., Rohrig, H.: Detecting cartoons: a case study in automatic video-genre classification. In: Proceeding of IEEE International Conference on Multimedia and Expro (ICME 2003), vol. 1, pp. 449–452 (2003)Google Scholar
  2. 2.
    Lyu, S., Farid, H.: How realistic is photorealistic? IEEE Transactions on Signal Processing 53, 845–850 (2005)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Ng, T.-T., Chang, S.-F., Hsu, J., Xie, L., Tsui, M.-P.: Physics-motivated features for distinguishing photographic images and computer graphics. In: Proceeding of ACM Multimedia, Singapore (November 2005)Google Scholar
  4. 4.
    Chen, W., Shi, Y.Q., Xuan, G.: Identifying computer graphics using HSV color model and statistical moments of characteristic functions. In: Proceeding of IEEE International Conference on Multimedia and Expo. (ICME 2007), Beijing, China, July 2-5 (2007)Google Scholar
  5. 5.
    Chen, W.: Detection of Digital Image and Video Forgeries, Ph.D. Dissertation, Department of Electrical and Computer Engineering, New Jersey Institute of Technology (2008)Google Scholar
  6. 6.
    Shi, Y.Q., Xuan, G., Zou, D., Gao, J., Yang, C., Zhang, Z., Chai, P., Chen, W., Chen, C.: Steganalysis based on moments of characteristic functions using wavelet decomposition, prediction-error image, and neural network. In: Proceeding of International Conference on Multimedia and Expo. (ICME 2005), Amsterdam, Netherlands (2005)Google Scholar
  7. 7.
    Chen, C., Shi, Y.Q., Chen, W., Xuan, G.: Statistical moments based universal steganalysis using JPEG-2D array and 2-D characteristic function. In: Proceeding of Proceeding of IEEE International Conference on Image Processing (ICIP 2006), Atlanta, Georgia (2006)Google Scholar
  8. 8.
    Leon-Garcia, A.: Probability and Random Processes for Electrical Engineering, 2nd edn. Addison-Wesley Publishing Company, Reading (1994)MATHGoogle Scholar
  9. 9.
    Pratt, W.K.: Digital Image Processing, 3rd edn. John Wiley & Sons, Inc., Chichester (2001)CrossRefMATHGoogle Scholar
  10. 10.
    Columbia University DVMM Research Lab: Columbia Photographic Images and Photorealistic Computer Graphics DatasetGoogle Scholar
  11. 11.
  12. 12.
    Abe, S.: Support Vector Machines for Pattern Classification, 1st edn. Springer, Heidelberg (2005)MATHGoogle Scholar
  13. 13.
    Friedma, F., Hastie, T.: Additive logistic regression: a statistical view of boosting. An Official Journal of the Institute of Mathematical Statistics (2002)Google Scholar
  14. 14.
    Tieu, K., Viola, P.: Boosting image retrieval. In: Proceeding of IEEE Conference on Computer Vision and Pattern Recognition (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Patchara Sutthiwan
    • 1
  • Jingyu Ye
    • 1
  • Yun Q. Shi
    • 1
  1. 1.Dept. of ECENew Jersey Institute of TechnologyNewarkUSA

Personalised recommendations