The rapidly changing nature of the New Psychoactive Substance (NPS) market and the overall large number of substances that need to be monitored have presented challenges for early warning activities in recent years. Since the number of illicit drug classes is growing exponentially over time, the current challenge is no longer to search substances during an investigation, but rather to provide a sort of preliminary identification of the substance as illicit, i.e., to tag it as such, especially when it is a new type of drug. Today several instruments equipped with up-to-date libraries allow for the rapid identification of drugs of abuse. However, this approach suffers from four main problems: 1) The instruments libraries are not updated regularly; 2) The instruments do not easily overcome the matrix effect; 3) NPSs are trafficked and sold in smaller doses, making their detection and identification more difficult; 4) Routine methods of analysis during forensic analysis are no longer effective in screening drugs of abuse, the parent drugs but also their metabolites, due to the lack of structural information and commercial reference materials. NARCOSIS (Non-tArgeted foRensic multidisCiplinary platfOrm for inveStigatIon of drug-related fatalitieS) will provide a set of features for fast, robust, and reliable multidisciplinary approach to harmonise forensic investigations by means of an up-to-date and updateable diagnostic platform with the following key features: 1) A set of selected orthogonal instruments operable both on-site and in laboratories (Raman/SERS, IR, HSI, HRMS) will be adapted to be used with the NARCOSIS platform; 2) Build a comparable and shareable (cross-organisation) reference spectra (meta-spectra) database for faster detection and identification of drugs of abuse; 3) Create an AI-assisted spectra management and integration toolkit for analytical measurements to support the EU Early Warning System to rapidly detect, assess and respond to NPS.

RITHMS project intends to boost the operational capacity of Police and Customs/Border Authorities in addressing the increasingly organised and poly-criminal nature of trafficking in cultural goods through research, technological innovation, outreach and training. Illicit trade in cultural heritage has progressed from a local phenomenon limited to the initiative of a few individuals to a highly remunerative source of income for criminal organisations and terrorist groups that have taken advantage of the opportunities offered by the web and social media to further expand an already flourishing market. Against this background, RITHMS proposes an interdisciplinary approach made necessary by the transnational character of this crime and its links to other criminal networks. The project will define a replicable strategy to counter the challenges in addressing the illicit trafficking of stolen/looted cultural goods and to investigate the mechanisms underpinning it, including its connection with organised crime. RITHMS will foster cross-cutting research bringing together all the domains relevant to expand the understanding of this type of crime (art market, criminology, law studies, forensic science, etc.). Inputs from these disciplines will inform the theoretical framework underlying an interoperable AI-based Platform able to identify criminal organised networks and to provide investigators with valuable intelligence on the activities and evolution of such networks. The Platform will be leveraging the methods of Social Network Analysis (SNA), a methodology that gives a way to better understand human behaviour through people's relations and interactions. Including four Police authorities, two Border Agencies, one Police School, SMEs, and researchers from SSH and ICT, RITHMS Consortium is well positioned to offer solutions to counteract more effectively organisations involved in illicit activities against cultural heritage.

Money underpins all terrorist activities (without money propaganda, training, recruitment, and support are less effective or null). The disruption of terrorist financing activity is a key element in any fight against terrorism. DANTE will deliver effective, efficient and automated data mining, analytics solutions and an integrated system to detect, retrieve, and analyse huge amounts of heterogeneous and complex multimedia and multi-language (in five languages) terrorist-related contents from both the Surface and the Deep Web, including the Dark Nets. The ultimate goal is to discover (by “connecting the dots”), analyse and monitor potential terrorist-related activities and people, with focus on online fund raising activities, but also considering propaganda, training and disinformation. The challenging results of DANTE are achievable by exploiting, improving and integrating several existing data mining and analysis tools (mostly provided and owned by the partners; some already with high TRL). These tools and services will be further evolved and enhanced to be put at the service of the requirements provided by LEA partners (Guardia Civil, Policia Judiciaria, Home Office, Carabinieri). Thus, starting from lab and pre-prototype tools and methods, a market ready product with higher TRL will be the major outcome of DANTE. The presence of end user partners in the consortium will allow the validation/optimisation of the solution and boost of its adoption in their operational circumstances. The DANTE system will be validated and demonstrated in three pilots under the direct responsibility of the involved LEAs in their operational environments with the aim of detecting, analysing and monitoring terrorist-related contents for (i) propaganda (including recruitment, incitement, radicalization and disinformation), (ii) training and (iii) fund raising. DANTE surpasses the needs of the call for ensuring TRL6 of the projects results, by including components that will reach TRL7.

AI is transforming law enforcement, offering new tools for policing but also enabling advanced criminal tactics that challenge traditional methods. The global nature of crime, including cyber threats, trafficking, and terrorism, calls for innovative solutions as LEAs face vast data volumes and increasingly sophisticated criminal activities. AI has raised concerns with deepfakes—highly realistic but fake audio, video, or text that can depict individuals saying or doing things they never did. Deepfakes pose serious risks, impacting politics, economy, and social trust. Examples include fabricated videos of political figures and voice-cloned audio for financial fraud, often spread through social networks to deceive and defraud on a large scale. Forensic institutes and courts struggle to differentiate authentic evidence from AI fabrications, especially in cases involving national security. Despite promising detection research, existing methods fall short as current models rely on limited, non-diverse datasets and produce results with limited legal admissibility. The DETECTOR initiative aims to address these challenges, supporting LEAs and forensic experts in analyzing altered media. It offers an integrated solution through cross-border collaboration among AI researchers, LEAs, forensic scientists, legal experts, and ethicists. DETECTOR’s goals include: developing specialized tools for detecting media manipulation, creating comprehensive datasets, researching digital evidence exchange across borders, engaging stakeholders, informing policymakers, and training forensic experts in digital media and AI. Through these efforts, DETECTOR seeks to safeguard digital evidence authenticity and enhance forensic capabilities to counter AI-driven media manipulation across Europe