基于统计量的计算机图形检测模型  

Computer Graphics Detection Model Based on Statistics

作　　者：张震[1,2] 边玉琨[1] 平西建[2] 康吉全[1] 

机构地区：[1]郑州大学电气工程学院,郑州450001 [2]解放军信息工程大学信息工程学院,郑州450002

出　　处：《计算机工程》2009年第15期173-175,共3页Computer Engineering

基　　金：国家自然科学基金资助项目"信息伪装安全性理论与隐写分析技术研究"(60473022)

摘　　要：自然图像和计算机图形的鉴别可采用模式识别的方法。采用统计矩特征量和基于颜色滤波阵列的统计量来建立模型,以捕获自然图像和计算机图形在图像内容上的不同相关性。选用哥伦比亚大学自然图像和计算机图形数据库来测试该模型,采用人工神经网络作为分类器进行训练和测试。实验结果表明,该模型的识别率高,稳定性好,具有较好的应用前景。Pattern recognition method can be used to discriminate photo images from computer graphics. In this paper, the model is established based on statistical moment features and statistics extracted from Color Filter Array（CFA）. This model can capture different correlation of image content between photo images and Computer Graphics（CG）. To evaluate the performance of the scheme, the model was further tested applying to the Columbia photographic images and photorealistic computer graphics dataset. Artificial neural network is chosen as a classifier to train and test the given images. Experimental results demonstrate that the model performs higher degree of accuracy, indicating that the proposed approach has better stability and possesses promising capability in discrimination of computer graphics from photo images.

关 键 词：自然图像 计算机图形 统计矩特征 颜色滤波阵列 人工神经网络 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]