Forensics Lab
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Computa*onal Forensics
Admission of Ar+ficial Intelligence Methodologies in Forensic Sciences:
Current and Future Needs
Katrin Franke
Norwegian Informa*on Security Laboratory (NISlab) Gjøvik University College
www.nislab.no
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Computa*onal Forensics Katrin Franke, PhD, Professor
kyfranke.com
Professor of Computer Science, 2010 PhD in Ar*ficial Intelligence, 2005 MSc in Electrical Engineering, 1994
Industrial Research and Development (15+ years) Financial Services and Law Enforcement Agencies
Courses, Tutorials and post-‐graduate Training: Police, BSc, MSc, PhD
Chair IAPR/TC6 – Computa*onal Forensics
IAPR Young Inves*gator Award, 2009 Interna*onal Associa*on of Pa^ern Recogni*on
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Computa*onal Forensics Current Situa+on Knowledge and intui*on of the human expert plays a central role in daily forensic casework.
Courtroom forensic tes*mony is oaen cri*cized by defense lawyers as lacking a scien*fic basis.
Huge amount of data, *de opera*onal *mes, and data linkage pose challenges.
Computa+onal Forensics, aka applying Ar*ficial Intelligence Methodologies in Forensic Sciences
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Computa*onal Forensics Computa+onal Methods
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Computa*onal Forensics Objec+ve
Study and development of computa*onal methods to – Assist in basic and applied research, e.g. to establish or prove the scien+fic basis of a par*cular inves*ga*ve procedure,
– Support the forensic examiner in their daily casework.
Modern crime inves*ga*on shall profit from the hybrid-‐intelligence of humans and machines.
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Computa*onal Forensics Admission of Computa+onal Forensics
1. Need of Automa*za*on, Standardiza*on, and Benchmarking
2. Need of Educa*on, Joint Research, and Development by Forensic and Computer Scien*st
3. Need of Legal Framework
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Computa*onal Forensics Automa+za+on, Standardiza+on, and Benchmarking
Increase Efficiency and Effec+veness
Perform Method / Tool Tes+ng regarding their Strengths/Weaknesses and their Likelihood Ra*o (Error Rate)
Gather, manage and extrapolate data, and to synthesize new Data Sets on demand.
Establish and implement Standards for data, work procedures and journal processes
Fulfillment of Daubert Criteria h^p://en.wikipedia.org/wiki/Daubert_Standard
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Computa*onal Forensics Joint Research & Development: Forensic and Computer Scien+st
Educa+on and training, Revealing the state-‐of-‐the art in *each* domain
Sources of informa+on on events, ac*vi*es and financing opportuni*es
Interna+onal forum to peer-‐review and exchange, e.g., IWCF workshops
Performance evalua+on, benchmarking, proof and standardiza+on of algorithms
Resources in forms of data sets, soUware tools, and specifica+ons e.g. data formats
New Insights on problem descrip*on and procedures
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Computa*onal Forensics Legal Framework ?!
Ques*ons on methods for dimensionality reduc+on – loss of relevant informa*on
Ques*ons on extracted numerical parameters – loss of informa*on due to inappropriate features
Ques*ons on the reliability of applied computa+onal method / tool
Ques*ons on the final conclusion due to “wrong” computa+onal results
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Computa*onal Forensics
Thank you for your considera+on of comments!
Gekng in touch WWW: kyfranke.com
Email: [email protected] Skype/gTalk: kyfranke
