基于二阶差分统计量的自然图像与计算机图形的鉴别

Discrimination between Natural Images and Photorealistic Computer Graphics Using Second-Order Difference Statistics

针对自然图像和计算机图形的鉴别问题,提出一种基于图像二阶差分统计量的鉴别方法.首先在HSV颜色空间提取图像及其校准图像的二阶差分信号和预测误差信号,在此基础上提取二阶差分信号的方差、峰度以及预测误差信号的1～4阶统计量,并将其作为分类特征,结合Fisher线性判别分析,实现2类图像的正确分类.实验结果表明,该方法可以有效地鉴别自然图像和计算机图形,与已有算法相比具有更高的识别率,且计算量小、易于实现.

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.