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ANCHISE was represented by ICCS in the RISE-SD 2023 conference

ANCHISE project was represented by ICCS in the “Research and Innovation Symposium for European SECURITY and Defense” , an international EU Research and Innovation event in the field of Disaster and crisis management, Critical Infrastructure protection, Border security and Defence Research. The audience included high-level representatives of the EU, governmental representatives, researchers, industry, practitioners, and European security and defense stakeholders. 

ICCS as technology provider of ANCHISE harnessed the advantage to present its work in the context of project to a wide audience and made two presentations about the tools which will be delivered in the context of the project.

The motivation behind the design and the development of these tools lies on the need of Law Enforcement Agencies to efficiently fight cultural heritage trafficking. ICCS presented two different components named ART-CH and the CTD-TRAC which aim at addressing the following two challenges:

  • The tracking and recovery of stolen goods present a formidable challenge to LEAs worldwide

  • Traditional investigation methods are often burdened by the maze of online sales and cross-border trafficking, leaving a gap for illicit activities to thrive

ART-CH: An Advanced Reasoning Tool for Fighting Trafficking of Cultural Heritage

ART-CH applies rule-based reasoning and reveals hidden relations relevant to the activities of looters/traffickers. Such relations can be found in the source and destination points of cultural artefacts, in the traits and activities of illicit traders, in the distribution channels of cultural property, in illicit online listings referring to artefacts, etc.

It is capable of inferring logical conclusions from stated facts as well as of determining whether those axioms are complete and consistent. ART-CH applies rule-based reasoning, that can reveal new relations between the existing instances. It also uses predefined rules in order to reveal relations between different events and logged information as well as every other existing entity in the provided data Infers new knowledge. It can drastically increase the investigation and anticipation capabilities of LEAs regarding the illicit trading of cultural property, underpinning activities for identifying the traffickers’ modus operandi, the source and destination places of looted or stolen artefacts, the distribution channels of traffickers, illicit marketplaces, flows of cultural property, etc.

CTD-TRAC: A Complex Threat Detection Tool for Detecting Illicit Trafficking of Cultural Artefacts

This component can create unified graphs which can help LEAs, archaeologists or other practitioners and end-users to timely detect suspicious activities related to the illicit trading of antiquities and cultural property. Through this kind of visualization, end users can easily identify correlations between different entities/activities and detect suspicious illegal trading activities and the suspects for these activities.

CTD-TRAC can potentially increase the investigation capabilities of LEAs under the execution of advanced Deep Learning algorithms, responsible for identifying structured relationships among the different types of entities/activities involved. For creating the unified graphs, CTD-TRAC uses predefined weights based on the existing connections between entities/activities and the number of existing alerts. 

In a nutshell the technology provided by ICCS can benefit:

  • LEAs by boosting up investigation capabilities, reducing reaction time, enhancing prevention, uncovering hidden patterns and correlations

  • Archaeologists by providing improved insights on looting activities in archaeological sites

  • Museums by offering better tracking and tracing of cultural heritage artifacts

  • Cultural Heritage Experts by enhancing cultural heritage monitoring

Following to  the RISE-SD 2023 conference,  a paper entitled “A Semantic Engine for Fighting Cultural Goods Crime” was created which has already been accepted for publication as a book chapter in the Springer series "Security Informatics and Law Enforcement".


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