Distinguishing computer graphics from photographic images using a multiresolution approach based on local binary patterns

Zhaohong Li, Zhenzhen Zhang, Yunqing Shi

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

With the ongoing development of rendering technology, computer graphics (CG) are sometimes so photorealistic that to distinguish them from photographic (PG) images by human eyes has become difficult. To this end, many methods have been developed for automatic CG and PG classification. In this paper, we present a simple, yet efficient, multiresolution approach to distinguish CG from PG based on uniform gray-scale invariant local binary patterns (LBPs) with the help of support vector machines (SVM). We select YCbCr as the color model. The original Joint Photographic Experts Group (JPEG) coefficients of Y, Cb, and Cr components and their prediction errors are used for two LBP operators. From each 2D array and each LBP operator, we obtain 59 uniform LBP features. In total, 12 groups of 59 features are obtained from each image. But after multiresolution analysis, we select six groups of 59 features for CG and PG classification. The proposed features have been tested with thousands of CG and PG. Classification accuracy reaches 95.1% with support vector machines and outperforms the state-of-the-art works.

Original languageEnglish (US)
Pages (from-to)2153-2159
Number of pages7
JournalSecurity and Communication Networks
Volume7
Issue number11
DOIs
StatePublished - Nov 1 2014

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications

Keywords

  • Computer graphics
  • Image authentication
  • Image forensics
  • Local binary patterns
  • Multiresolution analysis

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