Nowadays, the volume of the multimedia heterogeneous evidence presented for digital forensic analysis has significantly increased, thus requiring the application of big data technologies, cloud-based forensics services, as well as Deep Learning techniques. In digital forensics domain, Deep Neural Networks (DNN) has been applied for cybercrime investigation such as child abuse investigations, malware classification, and image forensics. This tutorial covers topics at the frontier of research on DNN models in the context of digital forensics. The goal is to explain the principles behind solving forensic problems and give practical means for engineers and researchers (whose main competences may lie elsewhere), to apply the most powerful methods that have been developed in the last years. It will be presented and practically demonstrated how to formulate and solve image classification with freely available software that will be distributed to the participants of the tutorial.

Lecturers

• Introduction and Overview – Prof. Luca Spalazzi
• Deep Learning for Digital Forensics: Datasets, Representation, and Tasks – Prof. Emanuele Frontoni
• Deep Learning with Python for Image Classification – Dr. Marina Paolanti

 

Target Audience

The intended audience is academicians, graduate students and industrial researchers who are interested in the state-of-the-art deep learning techniques for information extraction and summarization in large forensics datasets. Audience with mathematical and theoretical inclination will enjoy the course as much as the audience with practical tendency.