| Autor: |
Abate, D., Agapiou, A., Toumbas, K., Lampropoulos, A., Petrides, K., Pierdicca, R., Paolanti, M., Di Stefano, F., Felicetti, A., Malinverni, E. S., Zingaretti, P. |
| Předmět: |
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| Zdroj: |
International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences; 2023, Vol. 48 Issue M/2, p3-10, 8p |
| Abstrakt: |
The use of artificial intelligence (AI) has the potential to be highly effective in detecting and monitoring illegal trafficking of cultural heritage (CH) goods through image classification techniques, particularly on online marketplaces where the trade of stolen CH objects has become a major global issue. Traditional investigation methods are no longer adequate, but with the assistance of AI, law enforcement agencies and CH organizations can now boost monitoring capabilities to detect, track, and possibly recover stolen objects more efficiently. AI algorithms can indeed analyze images to identify unique features and characteristics that can be used to determine their authenticity and provenance. Additionally, AI can detect patterns and networks of illicit trafficking, and link stolen objects to their places of origin, facilitating the recovery process. In this context, the SIGNIFICANCE project (Stop Illicit Heritage Trafficking with Artificial Intelligence) has been specifically designed to increase the response capabilities of public authorities and police corps against the illicit trafficking of cultural goods perpetrated through internet channels (i.e., social platforms, web, and dark web). By leveraging the power of Deep Learning (DL), AI can help prevent the loss of invaluable cultural artifacts and ensure that they are returned to their rightful owners and places of origin. This paper presents the results reached by the SIGNIFICANCE AI framework on image datasets collected over the web and social media through crawling algorithms. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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