Recognizing the country where a picture has been taken has many potential applications, such as identification of fake news and prevention of disinformation campaigns. Previous works focused on the estimation of the geo-coordinates where a picture has been taken. Yet, recognizing in which country an image was taken could be more critical, from a semantic and forensic point of view, than estimating its spatial coordinates. In the above framework, this paper provides two contributions. First, we introduce the VIPPGeo dataset, containing 3.8 million geo-tagged images. Secondly, we used the dataset to train a model casting the country recognition problem as a classification problem. The experiments show that our model provides better results than the current state of the art. Notably, we found that asking the network to identify the country provides better results than estimating the geo-coordinates and then tracing them back to the country where the picture was taken.

Which Country is This Picture From? New Data and Methods For Dnn-Based Country Recognition / O. Alamayreh, G.M. Dimitri, J. Wang, B. Tondi, M. Barni - In: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)NEW YORK : IEEE, 2023. - ISBN 978-1-7281-6327-7. - pp. 1-5 (( convegno IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) : 4-10 June 2023 tenutosi a Rhodes Island, Greece nel 2023 [10.1109/ICASSP49357.2023.10094908].

Which Country is This Picture From? New Data and Methods For Dnn-Based Country Recognition

G.M. Dimitri;
2023

Abstract

Recognizing the country where a picture has been taken has many potential applications, such as identification of fake news and prevention of disinformation campaigns. Previous works focused on the estimation of the geo-coordinates where a picture has been taken. Yet, recognizing in which country an image was taken could be more critical, from a semantic and forensic point of view, than estimating its spatial coordinates. In the above framework, this paper provides two contributions. First, we introduce the VIPPGeo dataset, containing 3.8 million geo-tagged images. Secondly, we used the dataset to train a model casting the country recognition problem as a classification problem. The experiments show that our model provides better results than the current state of the art. Notably, we found that asking the network to identify the country provides better results than estimating the geo-coordinates and then tracing them back to the country where the picture was taken.
Image recognition; Urban areas; Semantics; Speech recognition; Benchmark testing; Signal processing; Task analysis
Settore INFO-01/A - Informatica
Settore IINF-05/A - Sistemi di elaborazione delle informazioni
2023
https://ieeexplore.ieee.org/document/10094908
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2434/1187530
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