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Distinguish computer generated and digital images: A CNN solution

机译:区分计算机生成和数字图像:CNN解决方案

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The development of computer graphics has promoted the creation of computer generated images (CG) to a degree of unrivaled realism. It is of benefit to some industries like games and movies, which is aiming at making photo-realistic images. At the same time, it brought attacks on many vision systems. An artist can modify one fake image using knowledge based on computer graphics to deceive most people, turning this into a very dangerous weapon. It is of great importance to differentiate a photo-realistic computer generated image from a real photograph (PG). This problem can be modeled as a binary classification problem. Given one image, we just predict a label like “CG” or “PG” on it. To address this classification problem, we propose a method based on convolutional network through transfer learning. We choose VGG and ResNet as our base network structure and develop different models. Current state-of-the-art approaches rely on hand-crafted feature whileweadopt a power convolutional network as an alternative and achieve the state-of-the-art performance. In comparison, our method is end to end andmore stable.
机译:计算机图形的开发促进了计算机生成的图像(CG)的创建程度,无与伦比的现实主义。它对游戏和电影等行业有益,这旨在制作照片逼真的图像。与此同时,它带来了许多视觉系统的攻击。艺术家可以使用基于计算机图形学的知识来修改一个假图像,以欺骗大多数人,将其变成一个非常危险的武器。从真正的照片(PG)分辨出照片 - 现实计算机生成的图像非常重要。此问题可以被建模为二进制分类问题。给定一个图像,我们只是预测它就像它的“CG”或“PG”一样。为了解决这个分类问题,我们通过转移学习提出了一种基于卷积网络的方法。我们选择VGG和RESNet作为我们的基础网络结构并开发不同的型号。目前最先进的方法依赖于手工制作的功能,而将电源卷积网络作为替代方案,实现最先进的性能。相比之下,我们的方法结束到底枥稳定。

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