论文标题
通过多个CNN的正交训练来检测GAN生成的图像
Detecting GAN-generated Images by Orthogonal Training of Multiple CNNs
论文作者
论文摘要
在过去的几年中，我们目睹了一系列深度学习方法的兴起，以产生看起来非常现实的合成图像。这些技术在电影界和艺术目的中被证明是有用的。但是，如果用来传播虚假新闻或产生伪造的在线帐户，它们也很危险。因此，检测图像是实际的照片还是合成生成的是紧迫的必要性。本文提出了基于卷积神经网络（CNN）集合的合成图像的检测器。我们考虑检测培训时无法使用技术生成的图像的问题。考虑到新的图像发生器的发布越来越频繁，这是一个常见的情况。为了解决这个问题，我们利用两个主要思想：（i）CNN应该提供正交的结果，以更好地为合奏做出贡献； （ii）原始图像比合成图像更好，因此在测试时间应该更好地信任它们。实验表明，追求这两个想法提高了NVIDIA新生成的stylegan3图像的探测器准确性，从未在培训中使用。
In the last few years, we have witnessed the rise of a series of deep learning methods to generate synthetic images that look extremely realistic. These techniques prove useful in the movie industry and for artistic purposes. However, they also prove dangerous if used to spread fake news or to generate fake online accounts. For this reason, detecting if an image is an actual photograph or has been synthetically generated is becoming an urgent necessity. This paper proposes a detector of synthetic images based on an ensemble of Convolutional Neural Networks (CNNs). We consider the problem of detecting images generated with techniques not available at training time. This is a common scenario, given that new image generators are published more and more frequently. To solve this issue, we leverage two main ideas: (i) CNNs should provide orthogonal results to better contribute to the ensemble; (ii) original images are better defined than synthetic ones, thus they should be better trusted at testing time. Experiments show that pursuing these two ideas improves the detector accuracy on NVIDIA's newly generated StyleGAN3 images, never used in training.
