CEPOL Research & Science Conference 2022 MRU, Vilnius

The ROXANNE platform for supporting Law Enforcement practitioners in criminal investigations by analysing multi-modal data
06-09, 14:00–14:20 (Europe/Vilnius), Lecture Room 1 - I-417

Law enforcement practitioners today face increasing challenges when suppressing organized crime activities. Based on the responses of more than 120 LEA practitioners around the world who participated in a global online survey that was conducted by the ROXANNE consortium in Spring 2020, key pain points were the vast amount of data to be processed for each case, which is increasingly heterogeneous, and that crime evolves and adapts to new realities becoming increasingly more complex.

The high velocity and vast size of collected data calls for an efficient way to collect, filter, analyse data and automate the most time-consuming tasks. ROXANNE combines multiple analytics on various modalities (e.g., audio, text) to support LEAs in their investigations, by focusing their attention on potentially relevant information hidden in large volumes of data. Key ROXANNE technologies include Speaker Identification (SID), Natural Language Processing (NLP), Video analysis (VA) and criminal Network Analysis (NA). Nevertheless, according to the same study above, 90% of the participants have not received any training at all around one or more of the technologies above, while their level of knowledge varies mostly from no knowledge at all to basic knowledge.

Furthermore, ROXANNE offers an intuitive user interface supporting (criminal) Network Analysis for understanding how entities interact, Timeline Analysis for uncovering patterns of interactions on the temporal dimension, Statistical Analysis for viewing data distributions and Advanced Filtering for narrowing down the results according to users’ criteria. Nevertheless, machine-driven analysis is not a silver bullet and its limitations (e.g., non-zero false positive rates) mandate human expertise and judgement to complete the investigation. Thus, ROXANNE shall allow practitioners to judge and update the outputs, improve the accuracy so that the AI-based technologies are improved in the long run.

In the proposed session we plan to give an overview of ROXANNE platform and demonstrate it using a synthetic dataset for a fictional drug-dealing case that includes more than 100 wiretapped calls, which was prepared by the ROXANNE consortium and has been used in several training sessions.

Dr Costas Kalogiros (male) is a senior researcher and project manager of AEGIS IT Research. He received his BSc in Computer Science in 2002, his MSc in Information Systems in 2004 and his PhD in Computer Science in 2009 from the Department of Computer Science of Athens University of Economics and Business (AUEB). His research interests focus on applying the economic theory during the whole lifecycle of ICT-enabled systems, from inception to analysis, design-time and run-time, for achieving desirable goals in an efficient manner. In the Security domain, he is managing the development of Forensics Visualisation Toolkit (FVT) for supporting investigators suppressing organized crime activities in the context of ROXANNE project, funded by the EU under the H2020 programme.