From BBC
The number one sign you’re watching an AI video
By Thomas Germain
It’s over. You’re going to fall for it. You probably have already. In the last six months, AI video-generators got so good that our relationship with cameras is about to melt. Here’s the best-case scenario: you’ll get fooled, over and over again, until you’re so fed up that you question every single thing you see. Welcome to the future.
But for now, there are still a few red flags to look out for. One stands out. If you see a video with bad picture quality – think grainy, blurry footage – alarm bells should go off in your head that you might be watching AI.
“It’s one of the first things we look at,” says Hany Farid, a computer-science professor at the University of California, Berkeley, a pioneer in the field of digital forensics and the founder of the deepfake detection company GetReal Security.
“The leading text-to-video generators like [Google’s] Veo and OpenAI’s Sora still produce small inconsistencies,” Farid says. “But it’s not six fingers or garbled text. It’s more subtle than that.”
Even today's most advanced models often introduce problems such as uncannily smooth skin textures, weird or shifting patterns in hair and clothing, or small background objects that move in impossible or unrealistic ways. It’s all easy to miss, but the clearer the picture is, the more likely you are to see those tell-tale AI errors...
The other two factors, resolution and quality, are related but different. Resolution refers to the number or size of pixels in an image, while compression is a process that reduces the size of a video file by throwing away detail, often leaving behind blocky patterns and blurred edges.
In fact, Farid says low-quality fakes are so compelling that the bad guys downgrade their work on purpose. “If I’m trying to fool people, what do I do? I generate my fake video, then I reduce the resolution so you can still see it, but you can make out all the little details. And then I add compression that further obfuscates any possible artefacts,” Farid says. “It’s a common technique.”
Hany Farid is a professor in the Department of Electrical Engineering & Computer Sciences and the School of Information at UC Berkeley.
