Distinguishing computer graphics from photographic images using local binary patterns

Authors:
Zhaohong Li;Jingyu Ye;Yun Qing Shi
Affiliations:
Department of Electrical and Computer Eng., New Jersey Inst. of Technology, Newark, NJ and School of Electronic and Information Eng., Beijing Jiaotong Univ., Beijing, China,Shanghai Key Laboratory ...;Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ;Department of Electrical and Computer Engineering, New Jersey Institute of Technology, Newark, NJ
Venue:
IWDW'12 Proceedings of the 11th international conference on Digital Forensics and Watermaking
Year:
2012

Citing 10
Cited 0

Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns

IEEE Transactions on Pattern Analysis and Machine Intelligence
LOCO-I: a low complexity, context-based, lossless image compression algorithm

DCC '96 Proceedings of the Conference on Data Compression
Physics-motivated features for distinguishing photographic images and computer graphics

Proceedings of the 13th annual ACM international conference on Multimedia
Detecting differences between photographs and computer generated images

SPPRA'06 Proceedings of the 24th IASTED international conference on Signal processing, pattern recognition, and applications
The 'Dresden Image Database' for benchmarking digital image forensics

Proceedings of the 2010 ACM Symposium on Applied Computing
Image tamper detection based on demosaicing artifacts

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Computer graphics classification based on Markov process model and boosting feature selection technique

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Discriminating computer graphics images and natural images using hidden Markov tree model

IWDW'10 Proceedings of the 9th international conference on Digital watermarking
LIBSVM: A library for support vector machines

ACM Transactions on Intelligent Systems and Technology (TIST)
Detection of digital image and video forgeries

Detection of digital image and video forgeries

Quantified Score

Hi-index	0.00

Visualization

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.