The ability of #AI tools to readily generate highly convincing "#deepfake" text, audio, images, and (soon) video is, arguably, one of the greatest near-term concerns about this emerging technology. Fundamental to any proposal to address this issue is the ability to accurately distinguish "deepfake" content from "genuine" content. Broadly speaking, there are two sides to this ability:

* Reducing false positives. That is, reducing the number of times someone mistakes a deepfake for the genuine article. Technologies to do so include watermarking of human and AI content, and digital forensics.

* Reducing false negatives. That is, reducing the number of times one believes content that is actually genuine content to be a deepfake. There are cryptographic protocols to help achieve this, such as digital signatures and other provenance authentication technology.

Much of the current debate about deepfakes has focused on the first aim (reducing false positives), where the technology is quite weak (AI, by design, is very good at training itself to pass any given metric of inauthenticity, as per Goodhart's law); also, measures to address the first aim often come at the expense of the second. However, the second aim is at least as important, and arguably much more technically and socially feasible, with the adoption of cryptographically secure provenance standards. One such promising standard is the C2PA standard https://c2pa.org/ that is already adopted by several major media and technology companies (though, crucially, social media companies will also need to buy into such a standard and implement it by default to users for it to be truly effective).