“You won’t be able to tell,” said Hao Li, a leading researcher on computer-generated video at the University of Southern California who founded Pinscreen in 2015. “With further deep-learning advancements, especially on mobile devices, we’ll be able to produce completely photo-real avatars in real time.”

The technology is a triumph of computer science that highlights the gains researchers have made in deep neural networks, complex algorithms that loosely mimic the thinking of the human brain.

Similar breakthroughs in artificial intelligence allowed University of Washington researchers to move US President Barack Obama’s mouth to match a made-up script and the chip maker Nvidia to train computers to imagine what roads would look like in different weather.

What used to take a sophisticated Hollywood production company weeks could soon be accomplished in seconds by anyone with a smartphone.

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Not available for a video chat? Use your lifelike avatar as a stand-in. Want to insert yourself into a virtual reality game? Upload your picture and have the game render your character.

Those are the benign applications.

Now imagine a phoney video of North Korean dictator Kim Jong-un announcing a missile strike. The White House would have mere minutes to determine whether the clip was genuine and whether it warranted a retaliatory strike.

What about video of a presidential candidate admitting to taking foreign cash? Even if proved fake, the damage could be irreversible.

In some corners of the internet, people are using open-source software to swap celebrities’ faces into pornographic videos, a phenomenon called Deep Fakes.

It’s not hard to imagine a world in which social media is awash with doctored videos targeting ordinary people to exact revenge, extort or to simply troll.

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In that scenario, where Twitter and Facebook are algorithmically flooded with hoaxes, no one could fully believe what they see. Truth, already diminished by Russia’s misinformation campaign and US President Donald Trump’s proclivity to label uncomplimentary journalism “fake news”, would be more subjective than ever.

The consequences could be devastating for the notion of evidentiary video, long considered the paradigm of proof given the sophistication required to manipulate it.

“This goes far beyond ‘fake news’ because you are dealing with a medium, video, that we traditionally put a tremendous amount of weight on and trust in,” said David Ryan Polgar, a writer and self-described tech ethicist.

“If you look back at what can now be considered the first viral video, it was the witnessing of Rodney King being assaulted that dramatically impacted public opinion. A video is visceral. It is also a medium that seems objective.”

To stop the spread of fake videos, Facebook, Google and Twitter would need to show they can make good on recent promises to police their platforms.

Tech companies have a financial incentive to promote sensational content. And as platforms rather than media companies, they’ve fiercely defended their right to shirk editorial judgment.

Critics question whether Facebook, Google and Twitter are prepared to detect an onslaught of new technology like machine-generated video.

“Platforms are starting to take 2016-style misinformation seriously at some levels,” said Aviv Ovadya, chief technologist at the Centre for Social Media Responsibility. “But doing things that scale is much harder.”

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Fake video “will need be addressed at a deeper technical infrastructure layer, which is a whole different type of ball game,” Ovadya said.

(Facebook and Twitter did not respond to interview requests. Google declined to comment.)

The problem is that there isn’t much in the way of safeguards.

Hany Farid, a digital forensics expert at Dartmouth College who often consults for law enforcement, said watching for blood flow in the face can sometimes determine whether video is real. Slight imperfections on a pixel level can also reveal whether it is genuine.

Over time, though, Farid said, artificial intelligence will undermine those clues, perpetuating a cat-and-mouse game between algorithms and investigators.

“I’ve been working in this space for two decades and have known about the issue of manipulated video, but it’s never risen to the level where everyone panics,” Farid said. “But this machine-learning-generated video has come out of nowhere and has taken a lot of us by surprise.”

“There’s basically not much anyone can do right now,” Li said about automated detection tools.

The same conundrum faced the software company Adobe years ago when it became clear that its photo-editing program, Photoshop, was also being used for trickery. The company looked into including tools that could detect if an image had been doctored. But Adobe ultimately abandoned the idea, determining that fraudsters could exploit the tool just as easily, said Kevin Connor, a former Adobe executive who now works with Farid.

“I think Photoshop is an overwhelmingly good thing,” Connor said. “But that doesn’t mean a good thing can’t be used for bad.”

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Proponents of artificial video say fake imagery is an old problem that’s regularly debunked. Consider the doctored photo that emerged in 2004 of presidential candidate John Kerry with Jane Fonda at an anti-Vietnam-war rally. Even an 1860 portrait of Abraham Lincoln turned out to be manipulated. The president’s body was replaced with that of a more heroic-looking John Calhoun.

The chance of stopping technology like computer-generated video from advancing is highly unlikely, experts say.

That means the onus is on those who read the news and those who report it to verify video the best they can. Students at a young age also need to be taught how to wade through news sources critically, said Nonny de la Pena, an early practitioner of immersive journalism, which often leans on virtual reality.

“To shy away from technology because of fears it can be dangerous is a huge mistake,” she said. “Technology is scary. You’re going to have negative consequences. But the positive potential far outweighs the bad.”