The use of the Internet to distribute CSEM is an abhorrent crime. Referrals from Online Service Providers are key to fighting CSE. OSPs, detection technologies and users reporting suspicious material are improving. However, this leads to an increase in the sheer volume of referrals coupled with the increase in the distribution of CSEM online that is pushing MS LEAs to their limits and affecting their their capacity to prevent harm to infants and children, rescue those in immediate danger, and investigate and prosecute perpetrators. The NCMEC process has improved LEA capability. But, a typical CSE case contains 1-3 TBs of video, 1–10 million images. Limited human resources, manual analysis and the 4,000% increase in referrals since 2014 obligates a new approach. GRACE will apply proven techniques in ML to the referral and analysis process while embracing the very technical, ethical and legal challenges unique to fighting CSE. GRACE will leverage resources already in place at EUROPOL and its 9 MS LEAs and attempt to provide results early, frequently and flexibly, prioritising easy wins in the research plan (e.g. deduplication). By applying Federated Learning approach to the challenge of optimising analysis and information flow, GRACE will enable cooperation between LEAs in improving their own capabilities and harness experiential knowledge. The results of GRACE will be handed back to EUROPOL and MS LEAs for unrestricted use in their missions, helping to ensure their future technological autonomy.

Criminals and terrorists use more and more data hiding methods (steganography) for concealing incriminating information in innocent-looking digital media files such as images, video, audio, and text files. UNCOVERs main objective is to fill existing gaps in the ability of Law Enforcement Agencies (LEAs) for detecting the presence of such hidden information (i.e., steganalysis). To carry out a full investigation into criminal and terrorist activities, LEAs currently use available (commercial) tools to detect hidden information in collected digital media. However, these tools detect only a limited number of hiding methods, are slow, and offer no indication of confidence. Moreover, many commercial tools lag a decade behind the scientific state-of-the-art. The members of UNCOVER are committed to bridge these gaps and thus substantially increase the technological autonomy of LEAs in the field of digital media steganalysis. With its consortium of 22 partners including LEAs, forensic institutes, leading researchers working at universities and research institutions, as well as industrial companies, UNCOVER sets out to outperform available steganalysis solutions in terms of performance (number of detectable steganographic methods, detection accuracy), usability, operational needs, privacy protection, and chain-of-custody considerations. The developed detection and investigation tools will be integrated into a flexible and user-friendly platform. End-users play a key role throughout the project cycle: from proposal writing over analysis of user requirements and tools development through the final evaluation. In particular, regular feedback cycles with LEAs, forensics institutes and external stakeholders will ensure that the developed solutions can be integrated into the daily criminal investigation pipeline of LEAs. A set of clearly defined Key Performance Indicators allows an objective evaluation of progress and end results against the defined objectives.

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.

The increasing complexity of security challenges combined with the accumulation of significant amounts of digital data calls for better and more widespread use of Artificial Intelligence (AI) capabilities for law enforcement agencies (LEAs). AI can provide benefits to LEAs at all levels given the right understanding, tools, data and protection while increased awareness of criminal misuse is providing an immediate and concerning threat that must be tackled rapidly. Furthermore, a community that brings together LEAs, researchers, industry, security practitioners and other actors in the security ecosystem under a coordinated and strategic effort is essential for the realisation of these efforts into operational practices. STARLIGHT presents an inclusive and sustainable vision for increasing the awareness, capability, adoption and long-term impact of AI in Europe for LEAs. Five strategic goals underpin STARLIGHT’s approach: (1) Improve the widespread UNDERSTANDing of AI across LEAs to reinforce their investigative and cybersecurity operations and the need to uphold legal, ethical and societal values; (2) Provide opportunities to LEAs to EXPLOIT AI tools and solutions in their operational work that are trustworthy, transparent and human-centric; (3) Ensure that LEAs can PROTECT their own AI systems through privacy- and security-by-design approaches, better cybersecurity tools and knowledge; (4) Raise LEAs’ expertise and capacity to COMBAT the misuse of AI-supported crime and terrorism; and (5) BOOST AI for LEAs in Europe through high-quality datasets, an interoperable and standardised framework for long term sustainability of solutions, and the creation of an AI hub for LEAs that supports a strong AI security industry and enhances the EU’s strategic autonomy in AI. STARLIGHT will ensure European LEAs lead the way in AI innovation, autonomy and resilience, addressing the challenges of now and the future, prioritising the safety and security of Europe for all.