Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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A prototype architecture of smart sensor network

2023 , Müller, Wilmuth , Pallmer, Dirk , Reinert, Frank , Masini, Andrea , Firpi, Stefano

Experiences from recent conflicts show the strong need for providing timely a precise situation picture in order to improve the situational awareness of commanders. Situational Awareness requires comprehensive information delivered at the right time and at the right place, which must be adapted to the user, his role and current task; it must be conforming to the communication and visualisation devices currently in use. Therefore, a lot of sensor data and corresponding information have to be considered, exploited and combined in a flexible way. Smart sensor suites comprising different multi-spectral imaging sensors as core elements as well as additional non-imaging sensors may contribute decisively to the needed complete situation picture. The smart sensor suites should be part of a smart sensor network - a network of sensors, databases, evaluation stations and user terminals. Its goal is to optimize the use of various information sources for military operations like situation assessment, intelligence, reconnaissance, target recognition and tracking. Such a smart sensor network will enable commanders to achieve higher levels of situational awareness. Such a smart sensor network will enable commanders to achieve higher levels of situational awareness based on increased flexibility in using combined smart sensors. This paper presents a prototype of an Open System Architecture based on a system-of-systems approach. The open system architecture enables combining different sensors in multiple physical configurations, like distributed sensors, co-located sensors combined in a single package, sensors mounted on a tower, sensors integrated in a mobile platform, and use of trigger sensors. The mode of operation is adaptable to a series of scenarios with respect to relevant objects of interest, activities to be observed, available transmission bandwidth, etc.

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Blockchain Technologies for Chain of Custody Authentication

2022 , Chandramouli, Krishna , Horincar, Roxana , Jacobe de Naurois, Charlotte , Pallmer, Dirk , Faure, David , Müller, Wilmuth , Demestichas, Konstantinos

Technological advances are rapidly and radically changing human society, with “smart” sensors and gadgets penetrating almost all facets of daily life. The mass availability of these technologies has resulted in an exponential increase in the quantity of generated data. Together with the simultaneous exponential growth in computing power, this has driven rapid advances in the application of machine learning (ML) and artificial intelligence (AI). These developments in the fields of data availability and AI technologies present even greater challenges for law enforcement and policing, while simultaneously opening huge opportunities. Complementing the benefits of these technologies by the law enforcement authorities, criminals are also equally exploiting technology. A part of the digital world is the increasing abundance of digital evidence; from CCTV footage to emails to phone records, evidence has now gone digital and there is a requirement to ensure it is accessible, readable, and has long-term integrity when current technology, systems, or formats have been replaced or decommissioned. There is a further requirement for a seamless interface between policing and the criminal justice system to ensure digital evidence can be presented easily and without delay. As the quantity of data being used in criminal investigations becomes increasing larger, there is a critical need to maintain records tracing the origin and processing of evidence collected in digital format to authenticate the validity of the evidence. Addressing this need, the multimediaanalysis and correlation engine for organized crime prevention and investigation (MAGNETO) project proposed the use of blockchain technologies for tracking and recording the processing of information within the big-data architecture of the criminal investigation platform. The novelty of the proposed framework relies on the use of semantic technologies for knowledge formalization and the use of immutable technology based on hashing solutions to prevent evidentiary modifications. The platform addresses the need for mitigating the impact of cognitive bias to ensure the investigation platform offers objectivity in processing evidence.

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Common Representational Model and Ontologies for Effective Law Enforcement Solutions

2020 , Kozik, Rafal , Choras, Michal , Pawlicki, Marek , Holubowicz, Witold , Pallmer, Dirk , Müller, Wilmuth , Behmer, Ernst-Josef , Loumiotis, Ioannis , Demestichas, Konstantinos , Horincar, Roxanna , Laudy, Claire , Faure, David

Ontologies have developed into a prevailing technique for establishing semantic interoperability among heterogeneous systems transacting information. An ontology is an unambiguous blueprint of a concept. For Artificial Intelligence, only the defined notions can be considered existent. Thus, in relation to AI, an ontology can be understood as part of a program which delineates a collection of descriptions. An ontology, therefore, correlates the labels of the entities in the universe of discourse with wording that holds meaning for humans, explaining what those labels signify, along with the precise principles that force the interpretation and semantic utilization of these labels. An ontology constitutes a proper statement of a logical theory. It is a crucial component of a system with the capability to process, manage, analyze, correlate and reason from the large datasets characterized by heterogeneity. This paper depicts the process of development of a Common Representational Model (CRM) on top of several ontologies, taxonomies and classifications to facilitate computational and data mining functionalities. The building blocks of said CRM are delineated in detail, as well as its application in a specific use case.

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The Identification and Creation of Ontologies for the Use in Law Enforcement AI Solutions - MAGNETO Platform Use Case

2019 , Kozik, Rafal , Choras, Michal , Pawlicki, Marek , Holubowicz, Witold , Pallmer, Dirk , Müller, Wilmuth , Behmer, Ernst-Josef , Loumiotis, Ioannis , Demestichas, Konstantinos , Horincar, Roxana , Laudy, Claire , Faure, David

Every single day more and more organizations face the challenge of finding a way to support their conduct with data. The flooding amounts of data currently available vastly outweigh human capabilities, thus Big Data processing becomes a pressing issue. This problem is especially prevailing for Law Enforcement Agencies (LEAs), where massive amounts of critical data are collected from heterogenous sources, often by various entities in different countries. Ontologies have been developed into a predominant technique for establishing semantic interoperability among heterogeneous systems which transact information. In this paper we propose the Magneto ontology - a solution built as a crucial part of the Magneto project. It has been developed on top of well-established ontologies dealing with people, events and security incidents, bearing in mind the heterogenous nature of the myriad of data sources as the starting point. Examples of the building blocks, a classification of the sources of data, an overview of the application in a specific use scenario and a discussion on the future use of the ontology will be given.

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Knowledge Engineering for Crime Investigation

2022 , Müller, Wilmuth , Mühlenberg, Dirk , Pallmer, Dirk , Zeltmann, Uwe , Ellmauer, Christian , Pérez Carrasco, Francisco José , Garcia, Alberto Garcia , Demestichas, Konstantinos , Peppes, Nikolaos , Touska, Despoina , Gkountakos, Konstantinos , Muńoz Navarro, Eva , Martinez, Santiago

Building upon the possibilities of technologies like ontology engineering, knowledge representational models, text mining, and semantic reasoning, our work presented in this paper, which has been performed within the collaborative research project PREVISION (Prediction and Visual Intelligence for Security Information), co-funded by the European Commission within Horizon 2020 programme, is going to support Law Enforcement Agencies (LEAs) in their critical need to exploit all available resources, and handling the large amount of diversified media modalities to effectively carry out criminal investigation. A series of tools have been developed within PREVISION which provide LEAS with the capabilities of analyzing and exploiting multiple massive data streams coming from social networks, the open web, the Darknet, traffic and financial data sources, etc. and to semantically integrate these into dynamic knowledge graphs that capture the structure, interrelations and trends of terrorist groups and individuals and OGCs. The paper at hand focuses on the developed ontology and the tools for text mining, Extract Transform Load, Semantic Reasoning and the knowledge base and knowledge visualization.

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Evolving from Data to Knowledge Mining to Uncover Hidden Relationships

2021 , Demestichas, Konstantinos , Remoundou, Konstantina , Loumiotis, Ioannis , Adamopoulou, Evgenia , Müller, Wilmuth , Pallmer, Dirk , Mühlenberg, Dirk , Kozik, Rafal , Choras, Michael , Faure, David , Horincar, Roxana , Brodie of Brodie, Edward Benedict , Jacobe de Naurois, Charlotte , Chandramouli, Krishna , Rosca, Alexandra

Nowadays, law enforcement agencies - LEAs - are forced to deal with extreme volumes of data, being in need to analyse from heterogeneous data sources, uncover hidden relationships, trends and patterns of incidents and ultimately reach solid evidence to be used in court. In this chapter, a system is presented that can assist LEA officers in fighting crime, that, following the collection of the primary data, it applies semantic reasoning tools that allow the system to relate pieces of data, based on their inner relationships, and extract new information based on the asserted facts and rules defined by the LEAs. Then, the results derived by the reasoners and the initial data are fused using appropriate tools including a trajectory, a person and an event fusion tool, to be finally visualized by the proposed bipartite graphs. The proposed system is expected to decrease the time required to solve a crime by LEA's officers.

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Machine learning for discovery analytics to support criminal investigations

2020 , Müller, Wilmuth , Pallmer, Dirk , Mühlenberg, Dirk , Loumiotis, Ioannis , Remoundou, Konstantina , Kosmides, Pavlos , Demestichas, Konstantinos

Over the last decades, criminal activities have progressively expanded into the information technology (IT) world, adding to the ""traditional"" criminal activities, ignoring political boundaries and legal jurisdictions. Building upon the possibilities of technologies like Big Data analytics, representational models, machine learning, semantic reasoning and augmented intelligence, our work presented in this paper, which has been performed within the collaborative research project MAGNETO (Technologies for prevention, investigation, and mitigation in the context of the fight against crime and terrorism), co-funded by the European Commission within Horizon 2020 programme, is going to support LEAs in their critical need to exploit all available resources and handling the large amount of diversified media modalities to effectively carry out criminal investigation. The paper at hand focuses at the application of machine learning solutions for information fusion and classification tools intended to support LEA's investigations. The Person Fusion Tool will be responsible for finding in an underlying knowledge graph different person instances that refer to the same person and fuse these instances. The general approach, the similarity metrics, the architecture of the tool and design choices as well as measures to improve the efficiency of the tool will be presented. The tool for classifying money transfer transactions uses decision trees. This is due to a requirement of easy explainability of the classification results, which is demanded from the ethical and legal perspective of the MAGNETO project. The design of the tool, the selected implementation and an evaluation based on anonymized financial data records will be presented.

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Knowledge Engineering and Ontology for Crime Investigation

2022 , Müller, Wilmuth , Mühlenberg, Dirk , Pallmer, Dirk , Zeltmann, Uwe , Ellmauer, Christian , Demestichas, Konstantinos

Building upon the possibilities of technologies like ontology engineering, knowledge representational models, and semantic reasoning, our work presented in this paper, which has been performed within the collaborative research project PREVISION (Prediction and Visual Intelligence for Security Information), co-funded by the European Commission within Horizon 2020 programme, is going to support Law Enforcement Agencies (LEAs) in their critical need to exploit all available resources, and handling the large amount of diversified media modalities to effectively carry out criminal investigation. A series of tools have been developed within PREVISION which provide LEAs with the capabilities of analyzing and exploiting multiple massive data streams coming from social networks, the open web, the Darknet, traffic and financial data sources, etc. and to semantically integrate these into dynamic knowledge graphs that capture the structure, interrelations and trends of terrorist groups and individuals and Organized Crime Groups (OCG). The paper at hand focuses on the developed ontology, the tool for Semantic Reasoning and the knowledge base and knowledge visualization.

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Multimedia analysis platform for crime prevention and investigation. Results of MAGNETO project

2021 , Perez, Francisco J. , Garrido, Victor J. , Garcia, Alberto , Zambrano, Marcelo , Kozik, Rafal , Choras, Michal , Mühlenberg, Dirk , Pallmer, Dirk , Müller, Wilmuth

Nowadays, the use of digital technologies is promoting three main characteristics of information, i.e. the volume, the modality and the frequency. Due to the amount of information generated by tools and individuals, it has been identified a critical need for the Law Enforcement Agencies to exploit this information and carry out criminal investigations in an effective way. To respond to the increasing challenges of managing huge amounts of heterogeneous data generated at high frequency, the paper outlines a modular approach adopted for the processing of information gathered from different information sources, and the extraction of knowledge to assist criminal investigation. The proposed platform provides novel technologies and efficient components for processing multimedia information in a scalable and distributed way, allowing Law Enforcement Agencies to make the analysis and a multidimensional visualization of criminal information in a single and secure point.

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Reasoning with small data samples for organised crime

2020 , Müller, Wilmuth , Pallmer, Dirk , Mühlenberg, Dirk , Loumiotis, Ioannis , Remoundou, Konstantina , Kosmides, Pavlos , Demestichas, Konstantinos

Building upon the possibilities of technologies like big data analytics, representational models, machine learning, semantic reasoning and augmented intelligence, our work presented in this paper, which has been performed within the collaborative research project MAGNETO (Technologies for prevention, investigation, and mitigation in the context of the fight against crime and terrorism), co-funded by the European Commission within Horizon 2020 programme, is going to support Law Enforcement Agencies (LEAs) in their critical need to exploit all available resources, and handling the large amount of diversified media modalities to effectively carry out criminal investigation. The paper at hand focuses at the application of machine learning solutions and reasoning tools, even with only small data samples. Due to the fact that the MAGNETO tools have to operate on highly sensitive data from criminal investigations, the data samples provided to the tool developers have been small, scarce, and often not correlated. The project team had to overcome these drawbacks. The developed reasoning tools are based on the MAGNETO ontology and knowledge base and enables LEA officers to uncover derived facts that are not expressed in the knowledge base explicitly, as well as discover new knowledge of relations between different objects and items of data. Two reasoning tools have been implemented, a probabilistic reasoning tool based on Markov Logic Networks and a logical reasoning tool. The design of the tools and their interfaces will be presented, as well as the results provided by the tools, when applied to operational use cases.

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