In recent years, the rapid advancements in artificial intelligence (AI) technologies have introduced methods for the quick generation of synthetic media with the potential for misleading the viewer about its source and what it represents. Although misinformation through manipulated media is not a new phenomenon, its prevalence and potential impact on eroded public trust have grown significantly due to the increasing realism of AI-generated media.  The Coalition for Content Provenance and Authenticity (C2PA) has emerged as a leading organization seeking to address these challenges through its standard of the same name.

This research examines the benefits and complexities of applying the C2PA standard to live media workflows, focusing on the integration into live video mixing processes such as overlays (e.g., face-blurring filters, superimposed logos) and dynamic updates to stream sources (e.g., switching camera feeds). The study evaluates both the opportunities and challenges associated with implementing C2PA in these contexts, including practical considerations such as the resources and computational overhead required for generating C2PA metadata, as well as organizational hardware and infrastructure requirements.

The findings contribute to the larger media community by providing concrete examples of how C2PA can be applied to live video workflows. Furthermore, this work offers valuable insights for standards development, guiding potential extensions of C2PA beyond its currently defined use cases. By enabling media broadcasting organizations to adopt the latest in provenance and authenticity technologies, this research lays the groundwork for fostering greater transparency and trust between media companies and their audiences.