This paper presents a case study on a gamified approach for evaluating AI-generated images, with a focus on recognition accuracy in a controlled, familiar environment. As generative AI becomes broadly accessible, concerns are growing about the creation of non-consensual or harmful content. Therefore, it is important to evaluate the capacity of AI to generate recognisable images of known people or animals, particularly in familiar and close settings. This case study aimed to investigate whether generative AI can accurately produce identifiable images based on brief textual descriptions and to explore the impact of cognitive biases and description quality on recognition. The study involved participants from the same workplace that were asked to provide textual descriptions of their colleagues and animals, which were used to generate images. These images were evaluated in a competitive quiz format to measure recognition accuracy. Findings reveal that familiarity significantly enhances recognition, though cognitive biases may influence participants' accuracy. Our gamified approach provides a streamlined and engaging framework for AI evaluation. However, the limited setting of the study and dataset suggest further research is needed to assess generalizability.

The 2025 Grand Challenge on Multimedia Verification addresses the challenges of verifying the authenticity and context of online multimedia content. Participants analyzed real-world cases of images and videos, assessed their sources, detected any potential manipulations, and submitted detailed verification reports. The main competition was structured into three stages: Training, Validation, and Real-World Verification, which included live test cases and welcomed solutions ranging from manual methods and OSINT practices to automated tools and novel AI techniques. A total of 32 teams from 11 countries registered. The submitted solutions presented diverse pipelines that integrated forensic analysis, multimodal reasoning, and large language models (LLMs). The results demonstrate significant progress toward semi-automated verification workflows, while also exposing challenges in scalability, consistency, and reliability. In this paper, we present the challenge design, datasets, evaluation criteria, and participant outcomes, providing insights to guide future research and practice in multimedia verification.

Newsrooms and journalists today rely on many different artificial-intelligence, big-data and knowledge-based systems to support efficient and high-quality journalism. However, making the different systems work together remains a challenge, calling for new unified journalistic knowledge platforms. A software reference architecture for journalistic knowledge platforms could help news organisations by capturing tried-and-tested best practices and providing a generic blueprint for how their IT infrastructure should evolve. To the best of our knowledge, no suitable architecture has been proposed in the literature. Therefore, this article proposes a software reference architecture for integrating artificial intelligence and knowledge bases to support journalists and newsrooms. The design of the proposed architecture is grounded on the research literature and on our experiences with developing a series of prototypes in collaboration with industry. Our aim is to make it easier for news organisations to evolve their existing independent systems for news production towards integrated knowledge platforms and to direct further research. Because journalists and newsrooms are early adopters of integrated knowledge platforms, our proposal can hopefully also inform architectures in other domains with similar needs.