Tracking Multiple Image Sharing on Social Networks

Social Networks (SN) and Instant Messaging Apps (IMA) are more and more engaging people in their personal relations taking possession of an important part of their daily life. Huge amounts of multimedia contents, mainly photos, are poured and successively shared on these networks so quickly that is not possible to follow their paths. This last issue surely grants anonymity and impunity thus it consequently makes easier to commit crimes such as reputation attack and cyberbullying. In fact, contents published within a restricted group of friends on an IMA can be rapidly delivered and viewed on a SN by acquaintances and then by strangers without any sort of tracking. In a forensic scenario (e.g., during an investigation), succeeding in understanding this flow could be strategic, thus allowing to reveal all the intermediate steps a certain content has followed. This work aims at tracking multiple sharing on social networks, by extracting specific traces left by each SN within the image file, due to the process each of them applies, to perform a multi-class classification. Innovative strategies, based on deep learning, are proposed and satisfactory results are achieved in recovering till triple up-downloads.