论文标题
使用深信念网络中的卫星图像中的操纵检测
Manipulation Detection in Satellite Images Using Deep Belief Networks
论文作者
论文摘要
随着商业卫星的增加，卫星图像更容易访问。这些图像用于广泛的应用中，包括农业管理，气象预测，自然灾害的损害评估和制图。包括手动编辑工具和自动化技术在内的图像操纵工具可以轻松地用于篡改和修改卫星图像。我们在本文中检查的一种类型的操作是剪接攻击，其中将一个图像（或同一图像）区域插入（剪接）中的一个区域。在本文中，我们提出了一种基于深度信念网络（DBN）的单级检测方法，用于拼接检测和本地化，而无需使用任何对操作的先验知识。我们评估方法的性能，并表明与其他方法相比，它在小伪造中提供了良好的检测和定位精度。
Satellite images are more accessible with the increase of commercial satellites being orbited. These images are used in a wide range of applications including agricultural management, meteorological prediction, damage assessment from natural disasters, and cartography. Image manipulation tools including both manual editing tools and automated techniques can be easily used to tamper and modify satellite imagery. One type of manipulation that we examine in this paper is the splice attack where a region from one image (or the same image) is inserted (spliced) into an image. In this paper, we present a one-class detection method based on deep belief networks (DBN) for splicing detection and localization without using any prior knowledge of the manipulations. We evaluate the performance of our approach and show that it provides good detection and localization accuracies in small forgeries compared to other approaches.
