Ask the person to turn their head left and right. Or to put on glasses. Combine simple tests like this and it becomes exponentially more complex for the gadget to pass them.

Like the Turing Test computational attack from Rick and Morty.

Rick speaking to a crowd of hologram characters: Everyone who's first name starts with an "L" who isn't Hispanic, walk in a circle the same number of times as the square root of your age times ten! - https://www.imdb.com/title/tt3333830/

My opinion on this is that we will "soon" have "trusted" webcams that create some sort of signature in each frame. In other words, a way to say "this is what the camera saw, guaranteed".

Probably something that would be built into phones and laptops first.

Of course, we know that messing with such a datastream would be easy for us, but for enterprise users (think liability) and news organizations it could be a real boon.

That's when you point your camera at a screen.

Will the "trusted" webcam manufacturers then attempt to detect any screen curvature?

Technically it would not be "this is what the camera saw, guaranteed" but "this is what the possessor of some signing key asserts they saw".

If a single shoddy manufacturer of cheap webcams somehow leaks a key that should have been on that webcam, then any software can easily encode a video stream with signatures asserting "this is what the camera saw, guaranteed".

Good question, maybe sign your videos with a key?

Or perhaps another neural network to distinguish the real from the fake?

Isn't that already done? They're called generative adversarial networks...

Yeah, the real reason Zoom should be actually be E2E encrypted.

Authenticity is about signing more than encrypting. But having that actually work would require a robust key distribution system that's actually usable by the general public.

If I understood it correctly, deepfakes are relatively easy to detect by software if you know what you’re looking for. I can imagine video conferencing software can implement this, and display a warning in similar ways that phishing emails are currently handled.

Deepfakes are implemented with Generative Adverserial Networks (GANs), where one component is a discriminative network that is already trying to distinguish real from fake, to provide feedback to the generation.

So I think any detection algorithms would get into a never-ending arms race.

None of the popular distributions use GANs, except that GANs have been used in experimental (later abandoned) modules. Unfortunately the Deepfake/GAN fallacy has been stuck in the Wikipedia entry for years.

The discriminative networks aren't very good at discriminating. I remember hearing people in the field saying they deliberately used under-powered discriminators because they got better generators that way. Was a year or two ago though so who knows if that's still true.

Spotting it in a static photo may be harder, but the artifacts will be really obvious in a video until the technology progresses considerably

Voight-Kampff test