Which country is this picture from? New data and methods for DNN-based country recognition
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Predicting the country where a picture has been taken from has many potential applications, like detection of false claims, impostors identification, prevention of disinformation campaigns, identification of fake news and so on. Previous works have focused mostly on the estimation of the geo-coordinates where a picture has been taken. Yet, recognizing the country where an image has been taken could potentially be more important, from a semantic and forensic point of view, than identifying its spatial coordinates. So far only a few works have addressed this task, mostly by relying on images containing characteristic landmarks, like iconic monuments. In the above framework, this paper provides two main contributions. First, we introduce a new dataset, the VIPPGeo dataset, containing almost 4 million images, that can be used to train DL models for country classification. The dataset contains only urban images given the relevance of this kind of image for country recognition, and it has been built by paying attention to removing non-significant images, like images portraying faces or specific, non-relevant objects, like airplanes or ships. Secondly, we used the dataset to train a deep learning architecture casting the country recognition problem as a classification problem. The experiments, we performed, show that our network provides significantly better results than current state of the art. In particular, we found that asking the network to directly identify the country provides better results than estimating the geo-coordinates first and then using them to trace back to the country where the picture was taken.
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