论文标题
用于检测具有本地化功能的GAN生成的洪水图像的体系结构
An Architecture for the detection of GAN-generated Flood Images with Localization Capabilities
论文作者
论文摘要
在本文中，我们解决了一项新的图像取证任务，即检测Climategan Architecture生成的假洪水图像。我们通过提出混合深度学习结构，包括检测和本地化分支，后者致力于识别Climategan操纵的图像区域。即使我们的目标是检测假洪水图像，实际上，我们发现添加本地化分支有助于网络专注于最相关的图像区域，并在对图像处理操作的概括能力和鲁棒性方面有了显着改善。在从Internet下载的两个原始洪水图像的数据集和Climategan生成的三个假洪水图像数据集中，通过大量不同的街道图像产生的假洪水图像的两个数据集进行了验证。
In this paper, we address a new image forensics task, namely the detection of fake flood images generated by ClimateGAN architecture. We do so by proposing a hybrid deep learning architecture including both a detection and a localization branch, the latter being devoted to the identification of the image regions manipulated by ClimateGAN. Even if our goal is the detection of fake flood images, in fact, we found that adding a localization branch helps the network to focus on the most relevant image regions with significant improvements in terms of generalization capabilities and robustness against image processing operations. The good performance of the proposed architecture is validated on two datasets of pristine flood images downloaded from the internet and three datasets of fake flood images generated by ClimateGAN starting from a large set of diverse street images.
