论文标题
卫星图像操纵检测的生成自回旋的合奏
Generative Autoregressive Ensembles for Satellite Imagery Manipulation Detection
论文作者
论文摘要
卫星图像由于越来越多的轨道商业卫星而变得越来越易于访问。许多应用程序都使用：农业管理，气象预测，自然灾害的损害评估或制图是其中的一些例子。不幸的是，这些图像可以轻松篡改和修改图像操纵工具损坏下游应用程序。由于应用于图像的操作的性质通常是未知的，因此首选不需要的无监督方法，这些方法不需要先验使用所使用的篡改技术。在本文中，我们使用生成自回旋模型的合奏来对图像像素的分布进行建模，以检测潜在的操作。与先前提出的方法相比，我们评估了获得准确定位结果的提出方法的性能。
Satellite imagery is becoming increasingly accessible due to the growing number of orbiting commercial satellites. Many applications make use of such images: agricultural management, meteorological prediction, damage assessment from natural disasters, or cartography are some of the examples. Unfortunately, these images can be easily tampered and modified with image manipulation tools damaging downstream applications. Because the nature of the manipulation applied to the image is typically unknown, unsupervised methods that don't require prior knowledge of the tampering techniques used are preferred. In this paper, we use ensembles of generative autoregressive models to model the distribution of the pixels of the image in order to detect potential manipulations. We evaluate the performance of the presented approach obtaining accurate localization results compared to previously presented approaches.
