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. 2024 Jul 2;14(1):15081.
doi: 10.1038/s41598-024-65885-6.

SIGNIFICANCE deep learning based platform to fight illicit trafficking of Cultural Heritage goods

Eva Savina Malinverni  1 , Dante Abate  2 , Antonia Agapiou  3 , Francesco Di Stefano  1 , Andrea Felicetti  4 , Marina Paolanti  5 , Roberto Pierdicca  1 , Primo Zingaretti  4
Affiliations

SIGNIFICANCE deep learning based platform to fight illicit trafficking of Cultural Heritage goods

Eva Savina Malinverni et al. Sci Rep. .

Abstract

The illicit traffic of cultural goods remains a persistent global challenge, despite the proliferation of comprehensive legislative frameworks developed to address and prevent cultural property crimes. Online platforms, especially social media and e-commerce, have facilitated illegal trade and pose significant challenges for law enforcement agencies. To address this issue, the European project SIGNIFICANCE was born, with the aim of combating illicit traffic of Cultural Heritage (CH) goods. This paper presents the outcomes of the project, introducing a user-friendly platform that employs Artificial Intelligence (AI) and Deep learning (DL) to prevent and combat illicit activities. The platform enables authorities to identify, track, and block illegal activities in the online domain, thereby aiding successful prosecutions of criminal networks. Moreover, it incorporates an ontology-based approach, providing comprehensive information on the cultural significance, provenance, and legal status of identified artefacts. This enables users to access valuable contextual information during the scraping and classification phases, facilitating informed decision-making and targeted actions. To accomplish these objectives, computationally intensive tasks are executed on the HPC CyClone infrastructure, optimizing computing resources, time, and cost efficiency. Notably, the infrastructure supports algorithm modelling and training, as well as web, dark web and social media scraping and data classification. Preliminary results indicate a 10-15% increase in the identification of illicit artifacts, demonstrating the platform's effectiveness in enhancing law enforcement capabilities.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
SIGNIFICANCE architecture detailing the interfaces between the different components as well as the data exchanged and functions between them.
Figure 2
Figure 2
Samples of SIGNIFICANCE dataset. (a) Coins. (b) Frescoes. (c) Icons. (d) manuscripts. (e) Other.
Figure 3
Figure 3
Example of ontological scheme for coins.
Figure 4
Figure 4
The sequence diagram of development of AI models.
Figure 5
Figure 5
Examples of dashboard resulting from the previous experiments. (a) Selection of products through metadata collected by crawlers. (b) Selection of products through metadata obtained by AI.
Figure 6
Figure 6
Examples of dashboard resulting from the previous experiments. Visualisation of the product and its metadata.
See this image and copyright information in PMC

References

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    1. Schloenhardt, A., Calderoni, F., Lelliott, J. & Weißer, B. UN Convention against Transnational Organized Crime: A Commentary (Oxford University Press, 2023).
    1. Lamotte KR. Unesco: Convention on the protection of the underwater cultural heritage. Int. Leg. Mater. 2002;41:37–56. doi: 10.1017/S0020782900009153. - DOI
    1. of Europe, C. & de l’Europe, C. Council of europe convention on offences relating to cultural property (nicosia, 19 may 2017). Uniform Law Rev.23, 656–691 (2018).

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