Blogs

NLP technologies against online crime

— by Garg, Shivam — last modified Apr 27, 2021 09:41 AM

Big Data in Artificial Intelligence (AI) and Machine Learning (ML) technologies offer new opportunities. The principle is that an extensive amount of data is required for the best result of the scenario of fighting crime ML models. In the ROXANNE project, the technical development targets to significantly enhance the criminal network analysis based on text, speech, language, and video technologies.

ROXANNE Stakeholder Board

— by Garg, Shivam — last modified Apr 01, 2021 11:00 AM

As an EU-funded collaborative research and innovative project, ROXANNE has been accompanied since its launch by a Stakeholder Board (SB) for independent and methodological advice, counseling the consortium on the practical implementation of its goals, as well as on encountered issues regarding its progress, direction, and outcomes. This external project advisory group is formed of 16 interdisciplinary experts coming from diverse backgrounds, including law enforcement, academia, EU entities, international organisations, fellow research projects, and industry.

Multimodal networks integration improves the criminal entity network construction with the help of deep neural networks

— by Garg, Shivam — last modified Mar 01, 2021 09:12 AM

The network integration fuses information from ROXANNE platform component technologies for network analysis. In previous workloads, crime-related networks from different data sources are constructed. We propose a multilayer and cross-network structural analysis to provide methods for integrating information from several investigation cases and linking the data records across these.

Making Natural Language Processing work for Little Training Data

— by Garg, Shivam — last modified Feb 22, 2021 11:07 AM

In the ROXANNE project we are solving real-world problems which often do not have a large labelled training set. To overcome such data scarcity problems, we use the combination of distant supervision and noise-robust learning algorithms. Distant supervision serves as a way of obtaining a large amount of labelled data in an automatic manner. Such data is however often noisy so that feeding them directly to DNN models will hinder the learning process. Nonetheless, noise-robust learning algorithms will help the models to focus on the correct instances in the training set so that the models will not be affected much by the noise.

The Benefits of Using Automatic Speaker Recognition in LEAs’ Investigative Processes

— by Garg, Shivam — last modified Feb 22, 2021 10:46 AM

Traditional manually performed forensic analyses are language-dependent and time-consuming, while automatic speaker recognition systems are fast and language-independent (nevertheless, the role of a forensic expert is still irreplaceable for the presentation of the results). There is also other voice biometric information that can be automatically extracted from a person’s voice, such as the gender of a speaker. The bottom line is that current automatic speaker recognition systems, such as the one currently built by the ROXANNE consortium, can greatly improve the analytical work efficiency, enabling law enforcement agencies of any size to investigate faster.

Probabilistic modelling for speaker recognition in criminal networks

— by Garg, Shivam — last modified Feb 05, 2021 04:01 PM

Speaker recognition is a technology that uses computer algorithms to analyze speech patterns and determine the identity of the speaker in a recording. Speaker recognition is an integral part of the ROXANNE platform because the identities of speakers in recordings from criminal investigations are usually not known. However, the speaker recognition scenario in criminal investigations differs from the most other speaker recognition scenarios because in addition to the audio of the recordings, we also have access to information about how the recordings are related. In particular, information about who has talked to whom is important. This information forms a network structure between the recordings. Naturally, it is important for the investigation to accurately uncover the network structure. Interestingly, using prior knowledge about the expected network structure in criminal networks may also improve the accuracy of speaker recognition.

Technology to enhance forensic speaker analysis

— by Garg, Shivam — last modified Feb 05, 2021 04:01 PM

Law enforcement agencies investigate criminal networks to find participants, understand their role in the network and, eventually, collect evidence for prosecution. Opportunities to identify criminals often lie in the traces that their communication leaves behind. A cell phone leaves many different traces, but the availability and usability of these modalities may vary. Therefore, it is worthwhile to invest in multiple modalities, increasing the chance of a useful result.