Identifying photorealistic computer graphics using second-order difference statistics

Discriminating photorealistic computer graphics from natural images is an important problem in image forensics. A new distinguishing method using second-order difference statistics is proposed in this paper. Firstly, the second-order difference signals and predicting error signals of both original and calibrated images are extracted in the HSV color space, and then the variance and kurtosis of second-order difference signals and the first four order statistics of predicting error signals are extracted to be used as distinguishing features, the Fisher linear discrimination analysis is used to construct a classifier to do the differentiating job. Experimental results show that the proposed method exhibits excellent performance for the discrimination between natural images and photorealistic computer graphics, outperforms previous proposed approaches. Moreover, it has a low computational complexity.

[1]  Shih-Fu Chang,et al.  Physics-motivated features for distinguishing photographic images and computer graphics , 2005, ACM Multimedia.

[2]  David Mumford,et al.  Statistics of natural images and models , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

[3]  Nasir D. Memon,et al.  Digital Image Forensics for Identifying Computer Generated and Digital Camera Images , 2006, 2006 International Conference on Image Processing.

[4]  Shih-Fu Chang,et al.  Classifying Photographic and Photorealistic Computer Graphic Images using Natural Image Statistics , 2006 .

[5]  Nasir D. Memon,et al.  New Features to Identify Computer Generated Images , 2007, 2007 IEEE International Conference on Image Processing.

[6]  Ying Wang,et al.  On Discrimination between Photorealistic and Photographic Images , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[7]  Siwei Lyu,et al.  How realistic is photorealistic , 2005 .

[8]  Andrew D. Ker.  Steganalysis of LSB matching in grayscale images , 2005, IEEE Signal Processing Letters.

[9]  Husrev T. Sencar,et al.  Overview of State-of-the-Art in Digital Image Forensics , 2007 .

[10]  Yun Q. Shi,et al.  Identifying Computer Graphics using HSV Color Model and Statistical Moments of Characteristic Functions , 2007, 2007 IEEE International Conference on Multimedia and Expo.

[11]  Feng Pan,et al.  Discriminating between photorealistic computer graphics and natural images using fractal geometry , 2009, Science in China Series F: Information Sciences.