To Authenticity, and Beyond! Building Safe and Fair Generative AI Upon the Three Pillars of Provenance

Provenance facts, such as who made an image and how, can provide valuable context for users to make trust decisions about visual content. Against a backdrop of inexorable progress in generative AI for computer graphics, over two billion people will vote in public elections this year. Emerging standards and provenance enhancing tools promise to play an important role in fighting fake news and the spread of misinformation. In this article, we contrast three provenance enhancing technologies—metadata, fingerprinting, and watermarking—and discuss how we can build upon the complementary strengths of these three pillars to provide robust trust signals to support stories told by real and generative images. Beyond authenticity, we describe how provenance can also underpin new models for value creation in the age of generative AI. In doing so, we address other risks arising with generative AI such as ensuring training consent, and the proper attribution of credit to creatives who contribute their work to train generative models. We show that provenance may be combined with distributed ledger technology to develop novel solutions for recognizing and rewarding creative endeavor in the age of generative AI.

Fake news and misinformation are major societal challenges that impact everything from journalism, to healthcare, and our upcoming elections in which over two billion people will vote worldwide in 2024. Authentic storytelling frequently relies on graphics and visual content. Yet, while advances in Generative Artificial Intelligence (GenAI) have democratized access to graphics creation and manipulation, GenAI has also lowered the entry bar for the abuse of visual content to spread misinformation.