Forensic reports use various types of conclusions, such as a categorical (CAT) conclusion or a likelihood ratio (LR). In order to correctly assess the evidence, users of forensic reports need to understand the conclusion and its evidential strength. The aim of this paper is to study the interpretation of the evidential strength of forensic conclusions by criminal justice professionals. In an online questionnaire 269 professionals assessed 768 reports on fingerprint examination and answered questions that measured self-proclaimed and actual understanding of the reports and conclusions. The reports entailed CAT, verbal LR and numerical LR conclusions with low or high evidential strength and were assessed by crime scene investigators, police detectives, public prosecutors, criminal lawyers, and judges. The results show that about a quarter of all questions measuring actual understanding of the reports were answered incorrectly. The CAT conclusion was best understood for the weak conclusions, the three strong conclusions were all assessed similarly. The weak CAT conclusion correctly emphasizes the uncertainty of any conclusion type used. However, most participants underestimated the strength of this weak CAT conclusion compared to the other weak conclusion types. Looking at the self-proclaimed understanding of all professionals, they in general overestimated their actual understanding of all conclusion types.
Victim-offender contact has been studied extensively in prisons, but research on contact between victims and mentally disordered offenders in forensic mental health settings is lacking. Therefore, an exploratory study was conducted on contact between victims and offenders in four Dutch forensic psychiatric hospitals. These offenders have committed serious (sexually) violent offenses, for which they could not be held fully responsible due to severe psychopathology. During the mandatory treatment, it is possible for offenders and their victims to engage in contact with each other if both parties agree to this. To explore the conditions under which this contact is suitable, we interviewed 35 social workers about their experiences in 57 cases from four Dutch forensic psychiatric hospitals. Findings demonstrated that, according to the social workers, no type of offense or psychopathology were obvious exclusion criteria for victim-offender contact. Social workers described offenders' problem awareness, stable psychiatric condition, and ability to keep to agreements as important factors that enable victim-offender contact. Implications and suggestions for future research are provided.
Are professionals better at assessing the evidential strength of different types of forensic conclusions compared to students? In an online questionnaire 96 crime investigation and law students, and 269 crime investigation and legal professionals assessed three fingerprint examination reports. All reports were similar, except for the conclusion part which was stated in a categorical (CAT), verbal likelihood ratio (VLR) or numerical likelihood ratio (NLR) conclusion with high or low evidential strength. The results showed no significant difference between the groups of students and professionals in their assessment of the conclusions. They all overestimated the strength of the strong CAT conclusion compared to the other conclusion types and underestimated the strength of the weak CAT conclusion. Their background (legal vs. crime investigation) did have a significant effect on their understanding. Whereas the legal professionals performed better compared to the crime investigators, the legal students performed worse compared to crime investigation students.
Every year the police are confronted with an ever increasing number of complex cases involving missing persons. About 100 people are reported missing every year in the Netherlands, of which, an unknown number become victims of crime, and presumed buried in clandestine graves. Similarly, according to NWVA, several dead animals are also often buried illegally in clandestine graves in farm lands, which may result in the spread of diseases that have significant consequences to other animals and humans in general. Forensic investigators from both the national police (NP) and NWVA are often confronted with a dilemma: speed versus carefulness and precision. However, the current forensic investigation process of identifying and localizing clandestine graves are often labor intensive, time consuming and employ classical techniques, such as walking sticks and dogs (Police), which are not effective. Therefore, there is an urgent request from the forensic investigators to develop a new method to detect and localize clandestine graves quickly, efficiently and effectively. In this project, together with practitioners, knowledge institutes, SMEs and Field labs, practical research will be carried out to devise a new forensic investigation process to identify clandestine graves using an autonomous Crime Scene Investigative (CSI) drone. The new work process will exploit the newly adopted EU-wide drone regulation that relaxes a number of previously imposed flight restrictions. Moreover, it will effectively optimize the available drone and perception technologies in order to achieve the desired functionality, performance and operational safety in detecting/localizing clandestine graves autonomously. The proposed method will be demonstrated and validated in practical operational environments. This project will also make a demonstrable contribution to the renewal of higher professional education. The police and NVWA will be equipped with operating procedures, legislative knowledge, skills and technological expertise needed to effectively and efficiently performed their forensic investigations.
Net als in het boek van Dan Brown, de ‘Da Vinci Code’, is de politie altijd op zoek naar aanwijzingen die naar de dader kunnen leiden. Waar in het boek allerlei cryptische symbolen en codes verborgen achtergelaten worden als aanwijzingen, zal in de praktijk bij forensisch onderzoek van de politie of het NFI, sporen gevonden moeten worden op een plaats delict. Het onderwerp van dit projectvoorstel, DaVinciQD, ligt op het dateren van een van dat soort sporen, namelijk vingersporen. Er wordt standaard in forensisch onderzoek naar vingersporen gezocht en indien gedetecteerd en veiliggesteld, worden zij ter plaatse of in het forensisch lab onderzocht en vervolgens vergeleken met een grote databank. Relevant is het om te bepalen of een vingerspoor afkomstig is van de dader en dus relevant voor het forensisch onderzoek. Om dit te bepalen is het niet alleen noodzakelijk om een vingerafdruk zichtbaar te maken en te koppelen aan een persoon, maar ook om deze te kunnen relateren aan het tijdsframe van het gepleegde misdrijf. Daarom de vraag om een methode te ontwikkelen die in staat is om vingerafdrukken te dateren. Het bepalen van het moment van achterlaten van een vingerspoor is cruciaal enerzijds om te bepalen of deze relevant is voor het lopende onderzoek, maar ook in de context van bewijsvoering en een eventuele veroordeling van een dader. Een consortium bestaande uit de onderzoeksgroepen Advanced Forensic Technology en NanoBio van Saxion, het Nederlands Forensisch Instituut, de Nationale Politie, de Universiteit Twente en enkele private bedrijven, zal een methode ontwikkelen om met behulp van quantum dots de datering van vingersporen mogelijk maken. De methode zal niet alleen in het lab, maar ook in de praktijk van de forensisch onderzoeker getest en gevalideerd worden.
The project aim is to improve collusion resistance of real-world content delivery systems. The research will address the following topics: • Dynamic tracing. Improve the Laarhoven et al. dynamic tracing constructions [1,2] [A11,A19]. Modify the tally based decoder [A1,A3] to make use of dynamic side information. • Defense against multi-channel attacks. Colluders can easily spread the usage of their content access keys over multiple channels, thus making tracing more difficult. These attack scenarios have hardly been studied. Our aim is to reach the same level of understanding as in the single-channel case, i.e. to know the location of the saddlepoint and to derive good accusation scores. Preferably we want to tackle multi-channel dynamic tracing. • Watermarking layer. The watermarking layer (how to embed secret information into content) and the coding layer (what symbols to embed) are mostly treated independently. By using soft decoding techniques and exploiting the “nuts and bolts” of the embedding technique as an extra engineering degree of freedom, one should be able to improve collusion resistance. • Machine Learning. Finding a score function against unknown attacks is difficult. For non-binary decisions there exists no optimal procedure like Neyman-Pearson scoring. We want to investigate if machine learning can yield a reliable way to classify users as attacker or innocent. • Attacker cost/benefit analysis. For the various use cases (static versus dynamic, single-channel versus multi-channel) we will devise economic models and use these to determine the range of operational parameters where the attackers have a financial benefit. For the first three topics we have a fairly accurate idea how they can be achieved, based on work done in the CREST project, which was headed by the main applicant. Neural Networks (NNs) have enjoyed great success in recognizing patterns, particularly Convolutional NNs in image recognition. Recurrent NNs ("LSTM networks") are successfully applied in translation tasks. We plan to combine these two approaches, inspired by traditional score functions, to study whether they can lead to improved tracing. An often-overlooked reality is that large-scale piracy runs as a for-profit business. Thus countermeasures need not be perfect, as long as they increase the attack cost enough to make piracy unattractive. In the field of collusion resistance, this cost analysis has never been performed yet; even a simple model will be valuable to understand which countermeasures are effective.
