论文标题
GAN生成图像的检测，归因和定位
Detection, Attribution and Localization of GAN Generated Images
论文作者
论文摘要
生成对抗网络（GAN）的最新进展导致创建了现实的数字图像，这对人类或计算机的检测构成了重大挑战。 gan被用于多种任务，从修改图像的小属性（Stargan [14]），在图像对之间传输属性（Cyclegan [91]），以及生成全新的图像（Progan [36]，stylegan [37]，Spade/spade/gaugan [64]）。在本文中，我们提出了一种新颖的方法来检测，属性和定位GAN生成的图像，将图像特征与深度学习方法结合在一起。对于每个图像，共同发生矩阵都是在不同方向（水平，垂直和对角线）的RGB通道的邻域像素上计算的。然后，对这些功能进行了深度学习网络的培训，以检测，属性和本地化这些GAN生成/操纵的图像。对我们在5个GAN数据集上进行的方法进行了大规模评估，其中包括超过276万张图像（Progan，Stargan，Cyclean，Stylegan和Spade/Gaugan），在检测GAN生成的图像方面显示出令人鼓舞的结果。
Recent advances in Generative Adversarial Networks (GANs) have led to the creation of realistic-looking digital images that pose a major challenge to their detection by humans or computers. GANs are used in a wide range of tasks, from modifying small attributes of an image (StarGAN [14]), transferring attributes between image pairs (CycleGAN [91]), as well as generating entirely new images (ProGAN [36], StyleGAN [37], SPADE/GauGAN [64]). In this paper, we propose a novel approach to detect, attribute and localize GAN generated images that combines image features with deep learning methods. For every image, co-occurrence matrices are computed on neighborhood pixels of RGB channels in different directions (horizontal, vertical and diagonal). A deep learning network is then trained on these features to detect, attribute and localize these GAN generated/manipulated images. A large scale evaluation of our approach on 5 GAN datasets comprising over 2.76 million images (ProGAN, StarGAN, CycleGAN, StyleGAN and SPADE/GauGAN) shows promising results in detecting GAN generated images.
