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

The Internet has become a key piece of any business activity. Criminal activity is not an exception. Some crimes previous to the Internet, such as thefts and scams, have found in the Internet the perfect tool for developing their activities. The Internet allows criminals hiding their real identity and the possibility to purchase specific tools for stealing sensitive data with a very low investment. The overall objective of RAMSES is to design and develop a holistic, intelligent, scalable and modular platform for Law Enforcement Agencies (LEAs) to facilitate digital Forensic Investigations. The system will extract, analyse, link and interpret information extracted from Internet related with financially-motivated malware. Customers, developers and malware victims will be included in order to obtain a better understanding of how and where malware is spread and to get to the source of the threat. To achieve these ambitious objectives, this project will rely on disruptive Big Data technologies to firstly extract and storage, and secondly look for patterns of fraudulent behaviour in enormous amounts of unstructured and structured data. We will focus on 2 case studies: ransomware and banking Trojans. In order to this, RAMSES brings together the latest technologies to develop an intelligent software platform, combining scraping of public and deep web, detecting manipulation and steganalysis for images and videos, tracking malware payments, extraction and analysis of malware samples and Big Data analysis and visualizations tools. Validation pilots will take place in three different EU countries (Portugal, Belgium and Spain) being the first a mono-LEA pilot in each site and the second a collaborative investigation pilot between several LEAs. Commercial potential will be validated during the project supported by a feasibility study to assess determinants for the adoption of the platform and appropriate business models.

Border authorities and Law Enforcement Agencies (LEAs) across Europe face important challenges in how they patrol and protect the borders. Their work becomes more problematic considering the heterogeneity of threats, the wideness of the surveyed area, the adverse weather conditions and the wide range of terrains. Although there are several research tools and works targeting these areas independently for border surveillance, nowadays border authorities do not have access to an intelligent holistic solution providing all aforementioned functionalities. Towards delivering such a solution, ROBORDER aims at developing and demonstrating a fully-functional autonomous border surveillance system with unmanned mobile robots including aerial, water surface, underwater and ground vehicles, capable of functioning both as standalone and in swarms, which will incorporate multimodal sensors as part of an interoperable network. The system will be equipped with adaptable sensing and robotic technologies that can operate in a wide range of operational and environmental settings. To provide a complete and detailed situational awareness picture that supports highly efficient operations, the network of sensors will include static networked sensors such as border surveillance radars, as well as mobile sensors customised and installed on board unmanned vehicles. To succeed implementing an operational solution, a number of supplementary technologies will also be applied that will enable the establishment of robust communication links between the command and control unit and the heterogeneous robots. On top of this, detection capabilities for early identification of criminal activities and hazardous incidents will be developed. This information will be forwarded to the command and control unit that will enable the integration of large volumes of heterogeneous sensor data and the provision of a quick overview of the situation at a glance to the operators, supporting them in their decisions.

Identity theft is rapidly expanding, causing substantial financial loss to millions of people all around the world. This invisible crime is also widespread across EU countries, where a growing number of citizens is targeted by sophisticated fraudulent attacks each year, both offline and online. 56% of Europeans have experienced at least one type of fraud in the last two years. European security officials speak of an “epidemic” created by a spike in demand from asylum-seekers and from terrorists carrying counterfeit documents to enter the EU. Security documents are increasingly being counterfeited or tampered with by criminals to facilitate transnational crime. The continued vulnerability of different types of identity and travel documents makes it extremely difficult to combat this problem. SafeTravellers value proposition aims at a) strengthening the security at the borders, b) improving the productivity of the Border Authorities and LEAs by providing them with the appropriate tools to combat identity fraud at the hardware, identity and travel document, and biometrics level, while c) offering a frictionless border crossing experience for EU/TCN citizens as they will not have to stop at the border checkpoints. SafeTravellers is both proposing a new way of citizen identification based on multiple biometrics instead of the problematic identity document, as well as an enhancement of the current way of identity verification at the borders through a set of tools that will detect attacks at the biometric hardware, identity and travel document fraud and attempts to falsify biometrics. The proposed solution is GDPR compliant and introduces various privacy-preserving mechanisms to safeguard the citizens' rights. Through the distributed European Multi-Biometric Data Space offered by SafeTravellers, each Member State will keep in its jurisdiction the personal data of its country nationals while allowing cross-border identity checks without transferring or revealing any biometric data.

LAGO will deliver the foundation for a trusted EU FTC Research Data Ecosystem (RDE) to address the so-called “Data Issue” in the FCT research landscape, i.e., the lack of domain-specific data in sufficient quality and quantity to enable appropriate training and testing of the developed methods, platforms and tools. LAGO will be instrumental in identifying common barriers and subsequently providing the structural, governance and technical foundations to foster and innovate data-oriented research collaboration among LEAs, security practitioners, relevant EU agencies, academic and industry researchers, policy makers and regulators. For this purpose, LAGO will develop an evidence-based and validated multi-actor Reference Architecture for the FCT RDE for these actors to deposit, share and co-create data and tools for FCT research purposes based on common rules, protocols, standards and instruments in a trusted and secured environment. The envisaged Reference Architecture and accompanying governance framework will be based on the design principles of decentralisation, data sovereignty, data quality, openness, transparency and trust and comply with EU values and principles on data protection, privacy and ethics. The Reference Architecture will be accompanied by a TRL-7 Reference Implementation of added-value technological tools to ensure practical realisation of the Reference Architecture as multiple data spaces and across the full range of concrete usage scenarios. A Roadmap will finally provide the consolidated rules, conditions and considerations for the actual deployment of the EU FCT RDE. The ultimate ambition of LAGO is to go beyond the creation of a common repository in order to innovate the FCT data-oriented research sphere by creation the crucial foundations for the sustainable, safe and trusted creation, co-creation, sharing and maintenance of training and testing datasets for the FCT research domain.