The widespread use of digital platforms, prioritising content based on engagement metrics and rewarding content creators accordingly, has contributed to the proliferation of disinformation and its far-reaching social and political impact. In addition, digital platforms often operate as black boxes, concealing their decision-making processes from users and prioritizing investor interests over ethical and social considerations. Consequently, this has contributed to the erosion of general trust in verification systems. To mitigate this issue, our project proposes a two-stage verification system. The first stage allows media industries to watermark their image and video content. The second stage involves implementing a machine-learning-based manipulation detection system for suspicious content. We present findings from an international user experience study, where potential online news consumers verified the authenticity of images on a prototype version of our system. In this paper, we reflect on critical issues of explainability addressed by participants in our user study and how we addressed this issue in the platform’s design.

Currently, digital media content is increasingly being used by cybercriminals for nefarious purposes. Such objects can be used, e.g., to covertly transfer malicious code to the infected host or to exfiltrate sensitive information from the secured perimeter to the attacker’s server. In this paper, we present the design and deployment of a web page harvesting platform that allows performing various types of large-scale analyses, including metadata inspection, detection of hidden data, or evaluation of compliance with the graphical standard. The platform architecture has a distributed, flexible, and modular form, making it easily extendable and efficient. In this article, we also include initial experimental results of the analyzes carried out on the content of 1,000 of the most popular websites.