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1 – Is Machine Learning Security IFS-business as usual?


Teddy Furon
Rennes, France

The talk conveys a vision of Machine Learning Security based on Information Forensics and Security.
In a nutshell, the IFS community protects three cardinal values (confidentiality, integrity, and property) of informative content, be it a transmitted signal (physical layer), an image (watermarking) or some text (fake news detection) for instance.
To what extend can we cast this framework onto Machine Learning? We will see that it makes sense for some scenarios (where we briefly summarise the state-of-the-art) although not covering all threats on Machine Learning.

T. F. (Senior Member, IEEE) received the M.Sc. and Ph.D. degrees in signal processing from Telecom ParisTech, in 1998 and in 2002, respectively. His research interests include the security related to multimedia, signal processing, and machine learning.
He has worked in industry (Thomson, Technicolor) and academia (Université catholique de Louvain, Belgium, and Inria Rennes, France, Linkmedia Team).
He co-founded of the company Imatag protecting rights of photo agencies.
He received the Brittany Best Young Researcher prize in 2006.
He is the coauthor of 80 conference articles, 20 journal articles, six book chapters, and nine patents.
He has been an Associate Editor for four journals, including IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY.
He was recently awarded the Security and Defense Chair of the French Defense Innovation Agency.

2 – Multimedia data recovery and its related workflows in digital forensics


Patrick De Smet
Belgian National Institute of Criminalistics and Criminology
Brussel, Belgium

This presentation provides an overview of several problems and solutions related to the reliable forensic recovery of multimedia data.
Whilst considering a brief overview of the academic state-of-the-art we also review several practical and possibly lesser known methods and tools that have been developed by forensic institutes and law enforcement agencies across Europe. Additionally, we demonstrate how these recovery methodologies interact with several real-life forensic IT analysis workflows such as multimedia authentication and steganalysis related investigations. Finally, we highlight several opportunities for future research both from the academic and the digital forensics case work point of view.
Dr. ir. Patrick De Smet received his MSc in computer science engineering at Ghent University (UGent) in 1995, after which he joined the Department for Telecommunications and Information Processing (TELIN). He obtained his PhD in computer science engineering at Ghent University in 2002 for his research on motion-based segmentation of video sequences and watershed-based image segmentation algorithms. In 2003 he started researching forensic ICT techniques and other investigative computing technologies. In 2005 he joined the Belgian national forensic science institute (NICC/INCC), where he continues his work on methods and tools for forensic 2D- and 3D-imaging analysis, (CCTV) image and video enhancement, and forensic IT data recovery and repair of damaged multimedia data. Currently, he is participating to the UNCOVER project in which he leads several steganography related R&D tasks. Patrick De Smet is a Member of IEEE and the IEEE Computer Society, and the current Chair of the ENFSI Digital Imaging Working Group.