Visual and Textual Analysis for Image Trustworthiness Assessment within Online News

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.