FIDOMAP is a novel multimedia content analysis platform designed to empower users in the fight against disinformation. At its core, FIDOMAP provides a user-friendly web interface that facilitates the detection and localization of manipulations and synthetic elements within visual content, utilizing state-of-the-art algorithms produced by multimedia forensics researchers. The FIDOMAP backend consists of a plugin-based software architecture where multiple forensic algorithms can be integrated. Each algorithm is packaged in a container and associated with a set of metadata that describes the supported types of files, the type of analysis performed, and the format of the results it produces.
Abstract To goal of PREMIER is to devise a new class of techniques capable of distinguishing fake from original videos. The new techniques will be developed by following a hybrid approach whereby forgery detectors based on deep learning are coupled with model-based methods, so to cope with the problems raising when deep learning is used within a multimedia forensics scenario. Looked-for solutions will aim at decreasing the amount of data needed for training, at providing ways to interpret the results of the forensic analysis, and at improving the security of the developed tools in the presence of an informed forger aiming at evading fake media detection.
Abstract The UNCHAINED project aims at designing a reliable forensics analysis framework able to characterize signatures left by different toolchains on digital images and videos. The core technical challenge of UNCHAINED is to build an information fusion system encompassing content-based statistics, container-based characteristics and additional side information (when available), to probabilistically map the media object under analysis first into a toolchain family and then into a specific toolchain, built upon a scalable set of basic operations.
Abstract The FENCE project aimed at designing a set of reliable forensic tools based on the visual analysis of image physical properties, to detect and locate several kinds of image manipulations working in a completely automatic way. The core technical challenge of FENCE was to build a computational architecture capable of assessing the physical integrity of an image included in a very large dataset, with no manual input or any other a priori information about its visual content.