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This series of images shows the output of Nvidia’s system over 18 days of processing. With its method called generative adversarial networks, researchers build a system that begins with low-resolution images and then gradually progresses to higher resolutions. This produces images that are sharp, detailed and convincing. PIC COURTESY OF NVIDIA

THE woman in the photo seems familiar. She looks like Jennifer Aniston, the Friends actress, or Selena Gomez, the child star turned pop singer. But, not exactly.

She appears to be a celebrity, one of the beautiful people photographed outside a movie premiere or an awards show. And yet, you cannot quite place her.

That’s because she’s not real. She was created by a machine.

The image is one of the faux celebrity photos generated by software under development at Nvidia, the big-name computer chipmaker that is investing heavily in research involving artificial intelligence (AI).

At a lab in Finland, a small team of Nvidia researchers recently built a system that can analyse thousands of celebrity snapshots, recognise common patterns and create new images that look much the same — but are a little different. The system can generate realistic images of horses, buses, bicycles, plants and many other common objects.

The project is part of a vast and varied effort to build technology that can automatically generate convincing images, or alter existing images in equally convincing ways.

The hope is that this technology can significantly accelerate and improve the creation of computer interfaces, games, movies and other media, eventually allowing software to create realistic imagery in moments rather than the hours — if not days — it can now take human developers.

In recent years, thanks to a breed of algorithm that can learn tasks by analysing vast amounts of data, companies like Google and Facebook have built systems that can recognise faces and common objects with an accuracy that rivals the human eye. Now, these and other companies, alongside many of the world’s top academic AI labs, are using similar methods to both recognise and create.

Nvidia’s images can’t match the resolution of images produced by a top-of-the-line camera, but when viewed on even the largest smartphones, they are sharp, detailed and, in many cases, remarkably convincing.

Like other prominent AI researchers, the Nvidia team believes the techniques that drive this project will continue to improve in the months and years to come, generating significantly larger and more complex images.

“We think we can push this further, generating not just photos but 3-D images that can be used in computer games and films,” said Jaakko Lehtinen, one of the researchers behind the project.

Today, many systems generate images and sounds using a complex algorithm called a neural network. This is a way of identifying patterns in large amounts of data.

By identifying common patterns in thousands of car photos, for instance, a neural network can learn to identify a car. But, it can also work in the other direction by using those patterns to generate its own car photos.

As it built a system that generates new celebrity faces, the Nvidia team went a step further in an effort to make them far more believable.

It set up two neural networks — one that generated the images and another that tried to determine whether those images were real, or fake. These are called generative adversarial networks, or GANs. In essence, one system does its best to fool the other — and the other does its best not to be fooled.

“The computer learns to generate these images by playing a cat-and-mouse game against itself,” Lehtinen said.

A second team of Nvidia researchers recently built a system that can automatically alter a street photo taken on a summer’s day so that it looks like a snowy winter scene. Researchers at the University of California, Berkeley, have designed another system that learns to convert horses into zebras and Monets into Van Goghs. DeepMind, a London-based AI lab owned by Google, is exploring technology that can generate its own videos. And, Adobe is fashioning similar machine-learning techniques with an eye towards pushing them into products like Photoshop, its popular image design tool.

But, new concerns come with the power to create this kind of imagery.

With so much attention on fake media these days, we could soon face an even wider range of fabricated images than we do today.

“The concern is that these techniques will rise to the point where it becomes very difficult to discern truth from falsity,” said Tim Hwang, who previously oversaw AI policy at Google and is now director of the Ethics and Governance of Artificial Intelligence Fund, an effort to fund ethical AI research.

Researchers are using a wide range of other machine-learning techniques to edit video in more convincing — and sometimes provocative — ways.

In August, a group at the University of Washington made headlines when it built a system that could put new words into the mouth of a Barack Obama video. Others, including Pinscreen, a California start-up, and iFlyTek of China, are developing similar techniques using images of United States President Donald Trump.

The results are not completely convincing. But, the rapid progress of GANs and other techniques point to a future where it becomes easier for anyone to generate faux images or doctor the real thing. That is cause for real concern among experts like Hwang. NYT

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