A framework for large-scale analysis of video ‘in the Wild’ to assist digital forensic examination


Digital forensics departments usually have to analyse vast amounts of audio-visual content, such as videos collected from street CCTV footage, hard drives or online resources. The framework presented in this article, which has been developed in the context of the EU-funded project LASIE, aims to assist investigators in their everyday tasks, through the provision of innovative tools for image and video analysis, object detection and tracking and event detection. These tools exploit the latest advances in machine learning, including deep neural networks, to handle the challenges in processing content from real-world data sources. The framework is enhanced with advanced inference and recommendation capabilities, which filter-out inconsistencies and recommend additional evidence. An intuitive user interface allows exploiting the capabilities of the available tools in a user-friendly manner. The framework supports distributed processing, with easy deployment of the services in clusters of multiple workstations, making the proposed solution appropriate for big data analytics tasks.

  • A. Axenopoulos, V. Eiselein, A. Penta, E. Koblents, E. La Mattina, P. Daras, T. Semertzidis, M. Lazaridis, A. Dimou, T. Senst, J. Amores, F. Alvarez, L. Kondrad, G. Vella, A. Randazzo, P. Pomo, "A framework for large-scale analysis of video ‘in the Wild’ to assist digital forensic examination", IEEE Security & Privacy Magazine, Special Issue on Digital Forensics, 17(1), 23-33, 2019. DOI: https://doi.org/10.1109/MSEC.2018.2875851

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