  The amount of multimedia content shared everyday, combined with the level of
realism reached by recent fake-generating technologies, threatens to impair the
trustworthiness of online information sources. The process of uploading and
sharing data tends to hinder standard media forensic analyses, since multiple
re-sharing steps progressively hide the traces of past manipulations. At the
same time though, new traces are introduced by the platforms themselves,
enabling the reconstruction of the sharing history of digital objects, with
possible applications in information flow monitoring and source identification.
In this work, we propose a supervised framework for the reconstruction of image
sharing chains on social media platforms. The system is structured as a cascade
of backtracking blocks, each of them tracing back one step of the sharing chain
at a time. Blocks are designed as ensembles of classifiers trained to analyse
the input image independently from one another by leveraging different feature
representations that describe both content and container of the media object.
Individual decisions are then properly combined by a late fusion strategy.
Results highlight the advantages of employing multiple clues, which allow
accurately tracing back up to three steps along the sharing chain.
