Computer generated images are visually becoming increasingly genuine, due to advances in technology as well as good graphic applications. Consequently, making distinction between computer generated images and natural images is no longer a simple task. Manual identification of computer generated images have failed to resolve the problems associated with legal issues on exact qualification of images. In this work, a colour range histogram was developed to categorise colours in computer generated images and natural images from a point of reference. Four groups were selected, using the algorithm, consisting of exact Red-Green-Blue (RGB) code (group 1), colour code within a range of 10 (group 2), colour code within a range of 20 (group 3) and colour code within a range of 30 (group 4) from the point of reference. An optimised equation for the four Colour Code Groups (CCG) was developed. The computer generated images categorised an average of 69.8%, 92.9%, 96.9% and 98.6%, of any colour code for groups 1, 2, 3 and 4, respectively. The categorised colours for natural images were 31.1%, 82.6%, 90.8% and 95.0% for groups 1, 2, 3 and 4, respectively. The results showed that natural images contain a wide range of RGB colours which makes them different. Consequently, the disparity in the percentage of colours categorised can be used to differentiate computer generated images from natural images.

中文翻译：

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用于自动图像源检测的颜色范围直方图技术

由于技术的进步以及良好的图形应用，计算机生成的图像在视觉上变得越来越真实。因此，区分计算机生成的图像和自然图像不再是一项简单的任务。计算机生成图像的手动识别未能解决与图像精确限定的法律问题相关的问题。在这项工作中，开发了颜色范围直方图，以从参考点对计算机生成的图像和自然图像中的颜色进行分类。使用算法选择了四组，包括精确的红绿蓝 (RGB) 代码（第 1 组）、10 范围内的颜色代码（第 2 组）、20 范围内的颜色代码（第 3 组）和从参考点起 30（组 4）范围内的颜色代码。开发了四个颜色代码组 (CCG) 的优化方程。计算机生成的图像分别对第 1、2、3 和 4 组的任何颜色代码进行了平均 69.8%、92.9%、96.9% 和 98.6% 的分类。对于第 1、2、3 和 4 组，自然图像的分类颜色分别为 31.1%、82.6%、90.8% 和 95.0%。结果表明，自然图像包含广泛的 RGB 颜色，这使它们与众不同。因此，分类颜色百分比的差异可用于区分计算机生成的图像与自然图像。分别为 2、3 和 4。结果表明，自然图像包含广泛的 RGB 颜色，这使它们与众不同。因此，分类颜色百分比的差异可用于区分计算机生成的图像与自然图像。分别为 2、3 和 4。结果表明，自然图像包含广泛的 RGB 颜色，这使它们与众不同。因此，分类颜色百分比的差异可用于区分计算机生成的图像与自然图像。