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.

The majority of news published online presents one or more images or videos, which make the news more easily consumed and therefore more attractive to huge audiences. As a consequence, news with catchy multimedia content can be spread and get viral extremely quickly. Unfortunately, the availability and sophistication of photo editing software are erasing the line between pristine and manipulated content. Given that images have the power of bias and influence the opinion and behavior of readers, the need of automatic techniques to assess the authenticity of images is straightforward. This paper aims at detecting images published within online news that have either been maliciously modified or that do not represent accurately the event the news is mentioning. The proposed approach composes image forensic algorithms for detecting image tampering, and textual analysis as a verifier of images that are misaligned to textual content. Furthermore, textual analysis can be considered as a complementary source of information supporting image forensics techniques when they falsely detect or falsely ignore image tampering due to heavy image postprocessing. The devised method is tested on three datasets. The performance on the first two shows interesting results, with F1-score generally higher than 75%. The third dataset has an exploratory intent; in fact, although showing that the methodology is not ready for completely unsupervised scenarios, it is possible to investigate possible problems and controversial cases that might arise in real-world scenarios.

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.