Sharing Device Identification on Images from Social Media Platforms

With social networks reaching unprecedented numbers of active users and data traffic worldwide, forensic scientists have been working to secure the trustworthiness of online information against the threat of misinformation. Several works have already investigated the possibility to trace malicious contents back to its perpetrators by following the chain of sharing operations. In real-world scenarios, however, users also have the chance to upload images in multiple ways, such as via smartphone apps or desktop browsers. Being able to detect different sharing modalities may represent a valuable and still unexplored insight for forensic purposes. Following this line, in this work we present SHADE, a first collection of real-world images shared from different devices, operating systems, and user interfaces. This database provides the forensic community with a new asset for investigating the peculiar artifacts introduced by different sharing modes, and for validating detection algorithms. The dataset was evaluated by applying a set of feature descriptors borrowed from platform provenance analysis, allowing us to reach promising results in the classification of sharing modalities.