Forensic science in 2018

Within the forensic science community much effort is taken to develop better standards and best practice guides for the different fields of forensic science. Also several ISO-standards are available or in development .

Currently much effort is conducted for forensic analysis directly at the crime scene, so the results are faster available. In the digital forensic field we see that much effort is taken to approach the data, though it is getting more complicated due to strong encryption due to privacy concerns of users. However, often solutions are found with hard work.

The use of databases of traces is progressing. Many databases exists for example for bio-metrics, such as DNA, fingerprints, speakers, faces and many other traces in forensic areas. The use of these databases should be expanded to other areas in forensic science, since this is important for providing statistical information, and also artificial intelligence algorithms can be trained by using these. Also I see that we need some algorithms for finding errors in forensic reports as well as court cases in order to improve forensic finding of future reports. For this reason also forensic data science is a field that is expanding rapidly and also many projects work on improving databases and knowledge in forensic science.

I look forward to much good progress within the forensic community and also hope on much collaboration, since we need to collaborate to solve the many challenges in forensic science.

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.

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.

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.

Databases and forensic science growing exponentially ?

The growth of different databases might have impact on forensic science, and certainly the validation. One of the issues of the data in the databases should be that they should be relied on and that the ground truth is known. Often the cleaning of data is necessary and the process should be followed according to strict rules to keep the integrity.

Nowadays we also see databases such as 23andme.com where the public can receive their genome data and in the Netherlands they even allow you to give you the inherited conditions, drug response, genetic risk factors and traits for informational purposes for 160 euro. In the United States this is not allowed anymore, since the FDA said the results are not validated. http://www.scientificamerican.com/article/23andme-is-terrifying-but-not-for-the-reasons-the-fda-thinks/  However of course it is interesting the privacy aspects are more an issue as was written in the Scientific American. These databases of over one million people is interesting for research (if permitted) though, however identities are not verified.

Several students finished their thesis on forensic investigation of drones, image manipulation and searching with open source methods through big forensic face databases. This month I also have some work, as a chairman of the ENFSI Forensic IT Working group in London, which will have a meeting from 20-22 September. And as usual before August 1st, I also should submit my proposals for presentations and a workshop at the American Academy of Forensic Sciences.

Mobile labs, big data and management in Forensic Science

One of the challenges in forensic science, is to receive results quickly after a crime happened. We will discuss in this blog three developments which help in speeding up the forensic investigation.

1. Mobile labs 
The development of labs on the crime scene is a challenging one. We can see several developments in DNA and chemistry to receive faster results after a crime has happened. Also in digital evidence we see several mobile solutions to collect the data from the crime scene. The lab-on-a-chip techniques are also contributing to faster results.

2. Big data
By using big data and analyzing the results from the past, it might be possible to have profiles of certain crimes. They can help to make a selection of techniques that work best for finding evidence at the crime scene. Since often over hundred traces are collected from the crime scene, a selection has to be made which should be analysed within 48 hours and other later.

3. Management of forensic labs
By using the right management of a forensic lab, the analysis might be conducted more efficient. Also backlogs should be prevented by using Service Level Agreements. Methods such as Lean Six Sigma can help, however most important is to spend time to the Human Resources and look that they are not overloaded with work also to maintain quality assurance as well.

The working group of ENFSI Forensic IT will have a meeting in London from 20-22 September 2016. This working group will be focussed on digital forensic science, and topics include mobile forensics, forensic data science, forensic big data platforms, chip forensics and analysis of location data.

Likelihood ratios, Digital Evidence and Big Data cloud systems, the paradox of security and forensic science

During the conference of Digital Forensic Research Workshop EU in Lausanne several topics were important. A wide range of new developments was published in the proceedings. During the keynote talks also attention was given to the issues with decryption of smart phones and several solutions were brought to attention, as well as the paradox that exists between security and digital evidence. Other solutions and issues were brought forward on collecting data from the cloud as digital evidence, as well as issues with time stamps that might occur.

A nice panel discussion on the different aspects of using Bayes and likelihood ratios provided different views on using it in digital evidence. Though one would not easily use this for dictionary attacks of passwords, it is used in interpretation of the evidence. The hypothesis of the defence and of the prosecutor should be clear however. And questions rise if the defence is able to provide a good hypothesis, since education and insights on different scenarios is needed. 

Also one topic of importance is that users of the reports should also understand the report and the conclusions drawn and interpret them correctly. In law systems with trained judged and prosecutors this might be easier then with a jury that is generally not trained in Bayes Theorem and the use of likelihood ratios. Some good guidelines of using this are provided by ENFSI.