Image Forensics in Online News

Recognizing fake images in online news is a challenging problem. This is especially true in the case of critical situations, when journalists might insert high-impact images to make a piece of news more appealing to the readers, neglecting to check their authenticity and provenance. Given the importance of this task, in the literature, it is possible to find several attempts to solve the problem from different points of view. This paper faces the specific problem of recognizing images in online news which have been modified or mis-contextualized, i.e. images taken in a different place and/or time with respect to the event to which they are associated. To identify image tampering a number of image forensic techniques were exploited and combined. On the other hand, for mis-contextualization detection, a textual analysis approach is proposed based on the extraction of features from the news the image is associated with, and from textual information retrieved online using the image at stake as pivot. The obtained results are rather satisfactory on laboratory data, with results that in some cases improve the state of the art for image forensics. The method was tested on three datasets, one of which already used in the literature, while the others created ad-hoc to further investigate its performances.