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 Forensics Lab
1 Katrin Franke, PhD, Professor Computa*onal Forensics   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 Forensics Lab
2 kyfranke.com 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 3 Forensics Lab
Computa*onal Forensics Computa+onal Methods Forensics Lab
4 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. Forensics Lab
5 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 Forensics Lab
6 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 o
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riteria h^p://en.wikipedia.org/wiki/Daubert_Standard 7 Forensics Lab
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 Forensics Lab
8 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 Forensics Lab
9 Computa*onal Forensics Thank you for your considera+on of comments! Gekng in touch WWW: kyfranke.com Email: [email protected] Skype/gTalk: kyfranke Forensics Lab
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