Identifying Computer Graphics using HSV Color Model and Statistical Moments of Characteristic Functions
Identifying Computer Graphics using HSV Color Model and Statistical Moments of Characteristic Functions
复制标题
DOI：
10.1109/icme.2007.4284852
复制
发表时间：
2007-07
期刊：
影响因子：
--
通讯作者：
Wen Chen;Y. Shi;Guorong Xuan
中科院分区：
文献类型：
--
作者：
Wen Chen;Y. Shi;Guorong Xuan
由高级渲染软件生成的计算机图形看起来如此逼真，以至于人们很难在视觉上将它们与摄影图像区分开来。因此，现代计算机图形学可以被用作一种令人信服的图像伪造形式。因此，识别计算机图形已成为图像伪造检测中的一个重要问题。本文介绍了一种新的方法来区分计算机图形和摄影图像。利用图像特征函数和小波子带的统计矩作为识别特征。此外，我们研究了不同的图像颜色表示的特征有效性的影响。具体而言，使用RGB和HSV颜色模型的效率进行了研究。实验结果表明，从HSV颜色空间中提取的特征，从色度分量的亮度，表现出更好的性能比RGB颜色模型。
Computer graphics generated by advanced rendering software come to appear so photorealistic that it has become difficult for people to visually differentiate them from photographic images. Consequently, modern computer graphics may be used as a convincing form of image forgery. Therefore, identifying computer graphics has become an important issue in image forgery detection. In this paper, a novel approach to distinguishing computer graphics from photographic images is introduced. The statistical moments of characteristic function of the image and wavelet subbands are used as the distinguishing features. In addition, we investigate the influence of different image color representations on the feature effectiveness. Specifically, the efficiency of using RGB and HSV color models is investigated. The experiments have shown that the features extracted from HSV color space, which decouples brightness from chromatic components, have demonstrated better performance than that from RGB color model.