[btown]

Retrospective antivirus-esque techniques are still useful, though, as not every actor is a state-level actor, and even then, forcing state-level actors to "burn" their state-of-the-art exploits/models because previous exploits/models are detected out-of-the-box, slows down the abuse of those actors.

And realistically, since deepfake detection will inevitably be more expensive than captchas or antivirus scanning, this will be adopted by human-in-the-loop organizations for critical processes where threat scoring or moderation is already being applied.

That said - Reality Defender, please train your system on diverse human data sets, do not release models where ethnicity or gender (including gender identity) are nontrivially correlated with deepfake score, and have processes in place from day 1 to allow users to report suspected patterns of bias. The kafkaesque "prove you're not a bot" scenario envisioned by the parent poster is one thing for holistic human-in-the-loop verification processes, and another thing if it suppresses minority voices and minority access to government services.

[bpcrd]

We agree. Dataset fidelity and bias are major concerns for publicly available datasets. For this reason we are working to develop programmatically created datasets along with anti-bias testing and policies.

[prometheus76]

"Bias" and "anti-bias" is a slippery snake that will bite you as soon as it warms up to you.