Saturday, September 23, 2017

The nicest job I know is being a forensic (data) scientist


The wide range of aspects a forensic scientist should handle, makes it for me a wide range of opportunities. The idea is to use the data and databases that exist useful for forensic interpretation and in the end use as evidence in court. In August I gave at the IAFS in Toronto a talk on deep learning and forensic evidence. To prepare data often scripting language such as Python is used, and we see lot of open source solutions being developed. Knowledge of databases i, and the handling of multimodal data is important. Also heterogeneous data as well as the veracity and validity of data is to consider.
The Netherlands Forensic Institute for me is a very nice place since I had my 25th year of celebration here and the organization is always changing in novel directions and giving new opportunities to learn and improve forensic science.
Since I work at the NFI and as a professor Forensic Data Science the University of Amsterdam, I also have several research projects (also for students) that work on digital evidence, as well as multimedia analysis. Also within European Horizon 2020 projects ASGARD and Marie Curie ESSENTIAL I am working on these topics combined with big data. International collaboration is important to optimize the solutions and prevent double work.
Currently I am also working on a special edition of the Journal Forensic Research of Taylor and Francis on digital evidence. It is an open access journal and the deadline is 1 december. If you have contributions, I would be happy to hear from you.
In the next months I am chairing the ENFSI Forensic IT Working group meeting in Barcelona from 7-10 November and I am also in the organizing committee of the EU IAI meeting in Amsterdam 12-13 October, so I look forward to see you there.

Sunday, April 30, 2017

Big video data, deep learning and forensic science

The increase of digital video data is still growing very fast, it is stated that 90 percent of the data on earth is created in the last two years. Also sensors of a self driving car could make 100 Gigabytes per second and suppose only a fraction is send to the cloud, then we have huge amounts of data that can be analyzed.


When this has to be analyzed we need fast methods for selection of relevant data. For humans it would not be feasible to process these amounts of data manually, so machine learning is one of the options to solve this. As chair of forensic data science at the Institute for Informatics of the University of Amsterdam this is one of the topics of research. Also combined with Biometrics where the privacy protection are top priority this makes new solutions possible for law enforcement.

It is also expected that more relevant statistical information is deducted from the data. However as always most of the data is heterogeneous and might be contaminated, so before drawing conclusion one should know a measure of uncertainty of the data.

One of the issues with deep learning is that part of it is a black box, and methods to explain how the network learns from the training sets are under development. However at the other side the human brain of an expert can also be seen as a black box, since by visual comparison the expert also uses previous experiences and is sensitive to bias. Research in this field is conducted and should also provide solutions to cope with this bias within forensic science.


Saturday, January 14, 2017

Antiforensic tools and criminal networks

In November I was the second reader of the PhD defense of the thesis of Michael Gruhn at the FAU University in Erlangen on rootkit and anti forensics software and how this can impact forensic science.
In December I was one of the promotors of the PhD defense of the thesis of Paul Duyn on criminal networks and a data driven approach on the different criminal networks as a complex adaptive system at the University of Amsterdam.
The combination of both approaches might even give more new insights, and nowadays there appears to be a growing interest in forensic data science since new approaches can be developed for preventing crimes from happening and examining crimes after they were committed. A multi-disciplinary approach is important to learn from each other fields and work on new solutions for example on cybercrime or any new crime that is developing. Even if antiforensics solutions have been used, possibilities exist to find forensic relevant information that can be used in court.
I look forward to many new multidisciplinary approaches, for example one of the approaches on forensic big data analysis is with the consortium Essential, were 15 PhD positions are available that will work on a range of topics within information policy and law.