AI-generated Images are disrupting existing approaches to verifying the trustworthiness of visual media. The application of generative AI in fields in which images are trusted visual evidence of persons, actions and events is drawing the attention of archival scientists and AI researchers. A literature review of AI-generated images as an emergent record format, identified an absence of archival and recordkeeping knowledge. Analysis of the results revealed six thematic categories: authenticity and verifiability; manipulation and misinformation; bias and representation; attribution and intellectual property; transparency and explainability; and ethical considerations. These themes inform the development of research questions and the next phase of the study that includes the application of theory and methods of archival diplomatics and computational archival science.

Thousands of videos are posted online every day. The affordability of video editing tools and social networks has facilitated the creation and spread of videos carrying disinformation, i.e. fake videos. Previous attempts to categorize disinformation have focused on content analysis and ascertaining the intention of creators. To extend these approaches, it is beneficial to incorporate the perspective of other fields that study the trustworthiness of records, such as archival science, to help detect and categorize fake videos. This paper proposes to leverage archival science in combination with computer engineering to devise a new framework for detecting and categorizing fake videos. In doing so, the paper offers a case study of the way in which Computational Archival Science, which blends archival and computational thinking, can be used to contribute to a novel approach towards solving the problem of fake videos.