With the recent tremendous advances of computer graphics rendering and image editing technologies, computergenerated fake images, which in general do not reflect what happens in the reality, can now easily deceive the inspection of human visual system. In this work, we propose a convolutional neural network (CNN)-based model to distinguish computergenerated (CG) images from natural images (NIs) with channel and pixel correlation. The key component of the proposed CNN architecture is a self-coding module that takes the color images as input to extract the correlation between color channels explicitly. Unlike previous approaches that directly apply CNN to solve this problem, we consider the generality of the network (or subnetwork), i.e., the newly introduced hybrid correlation module can be directly combined with existing CNN models for enhancing the discrimination capacity of original networks. Experimental results demonstrate that the proposed network outperforms state-of-the-art methods in terms of classification performance. We also show that the newly introduced hybrid correlation module can improve the classification accuracy of different CNN architectures.

中文翻译：

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使用通道和像素相关性区分计算机生成的图像和自然图像

随着最近计算机图形渲染和图像编辑技术的巨大进步，计算机生成的假图像通常不能反映现实中发生的情况，现在很容易欺骗人类视觉系统的检查。在这项工作中，我们提出了一种基于卷积神经网络 (CNN) 的模型，以通过通道和像素相关性将计算机生成 (CG) 图像与自然图像 (NI) 区分开来。所提出的 CNN 架构的关键组件是一个自编码模块，它将彩色图像作为输入，以明确地提取颜色通道之间的相关性。与之前直接应用 CNN 来解决这个问题的方法不同，我们考虑网络（或子网络）的通用性，即，新引入的混合相关模块可以直接与现有的 CNN 模型结合，以增强原始网络的判别能力。实验结果表明，所提出的网络在分类性能方面优于最先进的方法。我们还表明，新引入的混合相关模块可以提高不同 CNN 架构的分类精度。