Discrimination between natural images and photorealistic computer graphics using second-order difference statistics

In this paper, a new discrimination method using second-order difference statistics is proposed which is designed to distinguish natural images from photorealistic computer graphics. 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 discriminant 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 computer graphics, outperforms previous proposed methods with a low computational complexity.