What C2PA does and does not solve - by Yondon Fu

We're just a few months into 2024 and there does not appear to be any sign of slow down in generative AI advancements with the quality of synthetic media increasing every day across all modalities. As election seasons progress throughout the world, we can expect to see an uptick of synthetic deepfakes making their rounds online and with it increased concerns of how to distinguish between physical and AI generated realities.

In the past few months, C2PA, touted as a technical standard for establishing the provenance of digital media, has received increased attention as a potential solution with a number of tech companies including Meta, Google and OpenAI shipping integrations or announcing plans to do so. Adobe has been leading the charge promoting the use of Content Credentials, built using C2PA, as "nutrition labels" that can be used by consumers to understand the provenance of media and most importantly distinguish between real media and AI generated deepfakes. At the same time, the approach has been criticized for being too easily bypassed by bad actors and insufficient for proving provenance.

As the standard is evaluated and adopted, it is worthwhile asking: what are the exact problems that the C2PA standard actually solves for and what does it not solve for?

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