TY - GEN
T1 - Distinguishing computer graphics from photographic images using local binary patterns
AU - Li, Zhaohong
AU - Ye, Jingyu
AU - Shi, Yun Qing
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Image forensics
KW - computer graphics
KW - image authentication
KW - local binary patterns
UR - http://www.scopus.com/inward/record.url?scp=84883142401&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84883142401&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-40099-5_19
DO - 10.1007/978-3-642-40099-5_19
M3 - Conference contribution
AN - SCOPUS:84883142401
SN - 9783642400988
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 228
EP - 241
BT - Digital Forensics and Watermaking - 11th International Workshop, IWDW 2012, Revised Selected Papers
T2 - 11th International Workshop on Digital Forensics and Watermaking, IWDW 2012
Y2 - 31 October 2012 through 3 November 2012
ER -