Provenance 101: Introduction
A brief introduction to provenance: what C2PA Content Credentials are and why they work.
With the rapid advancement and proliferation of generative AI comes a flood of synthetic media that is increasingly realistic. Will Smith eating spaghetti now actually looks like Will Smith eating spaghetti!
This has profound implications — namely, that unless something is done, it will soon be impossible to tell what is real from what is fake. Take these two images of a bear for example; which one is real and which one is fake?
Governments are now codifying this concern into law, including the US, the EU, and China. For companies, this means that brand reputation now needs to be protected. For AI labs, this means that training data quality now needs to be preserved. For society, this means that the trust and truth we rely on now needs to be defended. And these actions must be taken at a larger scale than before.
#What Is Content Provenance?
The key to addressing this risk posed by generative AI is content provenance. Provenance is the set of facts we can determine about an item of content.
For example, if by watching a video we determine with our own eyes whether it is AI-generated or camera-captured, we are making a judgement on provenance. There is a subreddit, r/RealOrAI, dedicated to this ever-more-demanding task.
The value of content provenance lies not only in answering the “Real-or-AI” question, but also in the other facts tied to the content. An official post by a celebrity or a live broadcast by a news agency carries more weight than a random social media post, just as the value of art depends not only on the quality of the art but also on the identity of the artist.
For any organization that deals with digital content, there are two immediate business inquiries:
- How can we extract useful provenance from content?
- How can we use provenance to increase the value of our content?
#Detection versus Labeling
The most immediate solution is detection. Instead of manually processing content, an AI classification model is trained to predict whether content is “Real-or-AI” — and to do so at scale.
These models can be used immediately on any content, so even though their accuracy is limited (under 70% against top models), they can address inquiry (1) in settings like fraud detection, where the solution acts as a probabilistic filter. Two examples are GPTZero (for text and consumers) and Reality Defender (for media and enterprise).
The long-term solution is labeling. By adding a deliberate labeling step before publishing, the provenance of the content becomes far more powerful: more detailed, more reliable, more valuable. And instead of a probabilistic guess, there is a cryptographic proof.
Label-based content provenance has two main costs: complexity and investment. Trufo can abstract away the complexity, but content producers still need to make the investment of adding that extra labeling step into their workflows. The good news is that this investment is already in full swing, with companies like Google (via Pixel 10) and OpenAI (via ChatGPT) and many others already plugged into an emerging global ecosystem, centered on the C2PA open standard.