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)

Abstract

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.

Keywords

Image forensics computer graphics local binary patterns image authentication 

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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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