预测拍摄图片的国家有许多潜在的应用，例如对虚假索赔，冒名顶替者的识别，预防虚假信息运动，对假新闻的识别等等。先前的作品主要集中在拍摄图片的地理坐标的估计上。然而，从语义和法医学的角度来看，认识到已经拍摄图像的国家可能更重要，而不是确定其空间坐标。到目前为止，只有少数作品已经解决了这项任务，主要是依靠包含特征地标的图像，例如标志性的纪念碑。在上面的框架中，本文提供了两个主要贡献。首先，我们介绍了一个新的数据集，即Vippgeo数据集，其中包含近400万张图像，可用于训练DL模型进行国家分类。该数据集仅包含这种图像与国家识别的相关性，并且它是通过注意删除非显着图像（例如描绘面孔的图像或特定的非相关物体，例如飞机或船舶）来构建的。其次，我们使用数据集来训练深度学习架构，以将国家识别问题视为分类问题。我们执行的实验表明，我们的网络提供了比当前最新状态更好的结果。特别是，我们发现，要求网络直接识别该国提供比首先估算地理配位的更好的结果，然后使用它们将其追溯到拍摄图片的国家。

The detection of state-sponsored trolls acting in information operations is an unsolved and critical challenge for the research community, with repercussions that go beyond the online realm. In this paper, we propose a novel AI-based solution for the detection of state-sponsored troll accounts, which consists of two steps. The first step aims at classifying trajectories of accounts' online activities as belonging to either a state-sponsored troll or to an organic user account. In the second step, we exploit the classified trajectories to compute a metric, namely "troll score", which allows us to quantify the extent to which an account behaves like a state-sponsored troll. As a study case, we consider the troll accounts involved in the Russian interference campaign during the 2016 US Presidential election, identified as Russian trolls by the US Congress. Experimental results show that our approach identifies accounts' trajectories with an AUC close to 99\% and, accordingly, classify Russian trolls and organic users with an AUC of 97\%. Finally, we evaluate whether the proposed solution can be generalized to different contexts (e.g., discussions about Covid-19) and generic misbehaving users, showing promising results that will be further expanded in our future endeavors.

TU Dresden www.cityscapes-dataset.net train/val -fine annotation -3475 images train -coarse annotation -20 000 images test -fine annotation -1525 images