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What’s next for generative video

Pechoucek is especially worried about the malicious use of deepfakes in elections. During last year’s elections in Slovakia, for example, attackers shared a fake video that showed the leading candidate discussing plans to manipulate voters. The video was low quality and easy to spot as a deepfake. But Pechoucek believes it was enough to turn the result in favor of the other candidate.

A video made by Sora with the prompt: “A stylish woman walks down a Tokyo street filled with warm glowing neon and animated city signage. She wears a black leather jacket, a long red dress, and black boots, and carries a black purse. She wears sunglasses and red lipstick. She walks confidently and casually. The street is damp and reflective, creating a mirror effect of the colorful lights. Many pedestrians walk about.”


John Wissinger, who leads the strategy and innovation teams at Blackbird AI, a firm that tracks and manages the spread of misinformation online, believes fake video will be most persuasive when it blends real and fake footage. Take two videos showing President Joe Biden walking across a stage. In one he stumbles, in the other he doesn’t. Who is to say which is real?

“Let’s say an event actually occurred, but the way it’s presented to me is subtly different,” says Wissinger. “That can affect my emotional response to it.” As Pechoucek noted, a fake video doesn’t even need to be that good to make an impact. A bad fake that fits existing biases will do more damage than a slick fake that doesn’t, says Wissinger.

That’s why Blackbird focuses on who is sharing what with whom. In some sense, whether something is true or false is less important than where it came from and how it is being spread, says Wissinger. His company already tracks low-tech misinformation, such as social media posts showing real images out of context. Generative technologies make things worse, but the problem of people presenting media in misleading ways, deliberately or otherwise, is not new, he says.

Throw bots into the mix, sharing and promoting misinformation on social networks, and things get messy. Just knowing that fake media is out there will sow seeds of doubt into bad-faith discourse. “You can see how pretty soon it could become impossible to discern between what’s synthesized and what’s real anymore,” says Wissinger.

4. We are facing a new online reality.

Fakes will soon be everywhere, from disinformation campaigns, to ad spots, to Hollywood blockbusters. So what can we do to figure out what’s real and what’s just fantasy? There are a range of solutions, but none will work by themselves.

The tech industry is working on the problem. Most generative tools try to enforce certain terms of use, such as preventing people from creating videos of public figures. But there are ways to bypass these filters, and open-source versions of the tools may come with more permissive policies.

Companies are also developing standards for watermarking AI-generated media and tools for detecting it. But not all tools will add watermarks, and watermarks can be stripped from a video’s metadata. No reliable detection tool exists either. Even if such tools worked, they would become part of a cat-and-mouse game of trying to keep up with advances in the models they are designed to police.

Originally Appeared Here

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