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‘Deepfake’ technology can now create real-looking human faces

A new study from Nvidia researchers show just how far artificial image-generation technology has come in recent years.

Karros et al.
  • In 2014, researchers introduced a novel approach to generating artificial images through something called a generative adversarial network.
  • Nvidia researchers combined that approach with something called style transfer to create AI-generated images of human faces.
  • This year, the Department of Defense said it had been developing tools designed to detect so-called 'deepfake' videos.

A new paper from researchers at Nvidia shows just how far AI image generation technology has come in the past few years. The results are pretty startling.

Take the image below. Can you tell which faces are real?

Karros et al.

Actually, all of the above images are fake, and they were produced by what the researchers call a style-based generator, which is a modified version of the conventional technology that's used to automatically generate images. To sum up quickly:

In 2014, a researcher named Ian Goodfellow and his colleagues wrote a paper outlining a new machine learning concept called generative adversarial networks. The idea, in simplified terms, involves pitting two neural networks against each other. One acts as a generator that looks at, say, pictures of dogs and then does its best to create an image of what it thinks a dog looks like. The other network acts as a discriminator that tries to tell fake images from real ones.

At first, the generator might produce some images that don't look like dogs, so the discriminator shoots them down. But the generator now knows a bit about where it went wrong, so the next image it creates is slightly better. This process continues until, in theory, the generator creates a good image of a dog.

What the Nvidia researchers did was add to their generative adversarial network some principles of style transfer, a technique that involves recomposing one image in the style of another. In style transfer, neural networks look at multiple levels of an image in order to discriminate between the content of the picture and its style, e.g. the smoothness of lines, thickness of brush stroke, etc.

Here are a couple examples of style transfer.

In the Nvidia study, the researchers were able to combine two real images of human faces to generate a composite of the two. This artificially generated composite had the pose, hair style, and general face shape of the source image (top row), while it had the hair and eye colors, and finer facial features, of the destination image (left-hand column).

The results are surprisingly realistic, for the most part.

Karros et al.

​Concerns over 'deepfake' technology

The ability to generate realistic artificial images, often called deepfakes when images are meant to look like recognizable people, has raised concern in recent years. After all, it's not hard to imagine how this technology could allow someone to create a fake video of, say, a politician saying something abhorrent about a certain group. This could lead to a massive erosion of the public's willingness to believe anything that's reported in the media. (As if concerns about 'fake news' weren't enough.)

To keep up with deepfake technology, the Department of Defense has been developing tools designed to detect deepfake videos.

"This is an effort to try to get ahead of something," said Florida senator Marco Rubio in July. "The capability to do all of this is real. It exists now. The willingness exists now. All that is missing is the execution. And we are not ready for it, not as a people, not as a political branch, not as a media, not as a country."

However, there might be a paradoxical problem with the government's effort.

"Theoretically, if you gave a [generative adversarial network] all the techniques we know to detect it, it could pass all of those techniques," David Gunning, the DARPA program manager in charge of the project, told MIT Technology Review. "We don't know if there's a limit. It's unclear."

Hulu's original movie "Palm Springs" is the comedy we needed this summer

Andy Samberg and Cristin Milioti get stuck in an infinite wedding time loop.

Gear
  • Two wedding guests discover they're trapped in an infinite time loop, waking up in Palm Springs over and over and over.
  • As the reality of their situation sets in, Nyles and Sarah decide to enjoy the repetitive awakenings.
  • The film is perfectly timed for a world sheltering at home during a pandemic.
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Two MIT students just solved Richard Feynman’s famed physics puzzle

Richard Feynman once asked a silly question. Two MIT students just answered it.

Surprising Science

Here's a fun experiment to try. Go to your pantry and see if you have a box of spaghetti. If you do, take out a noodle. Grab both ends of it and bend it until it breaks in half. How many pieces did it break into? If you got two large pieces and at least one small piece you're not alone.

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Our ‘little brain’ turns out to be pretty big

The multifaceted cerebellum is large — it's just tightly folded.

Image source: Sereno, et al
Mind & Brain
  • A powerful MRI combined with modeling software results in a totally new view of the human cerebellum.
  • The so-called 'little brain' is nearly 80% the size of the cerebral cortex when it's unfolded.
  • This part of the brain is associated with a lot of things, and a new virtual map is suitably chaotic and complex.

Just under our brain's cortex and close to our brain stem sits the cerebellum, also known as the "little brain." It's an organ many animals have, and we're still learning what it does in humans. It's long been thought to be involved in sensory input and motor control, but recent studies suggests it also plays a role in a lot of other things, including emotion, thought, and pain. After all, about half of the brain's neurons reside there. But it's so small. Except it's not, according to a new study from San Diego State University (SDSU) published in PNAS (Proceedings of the National Academy of Sciences).

A neural crêpe

A new imaging study led by psychology professor and cognitive neuroscientist Martin Sereno of the SDSU MRI Imaging Center reveals that the cerebellum is actually an intricately folded organ that has a surface area equal in size to 78 percent of the cerebral cortex. Sereno, a pioneer in MRI brain imaging, collaborated with other experts from the U.K., Canada, and the Netherlands.

So what does it look like? Unfolded, the cerebellum is reminiscent of a crêpe, according to Sereno, about four inches wide and three feet long.

The team didn't physically unfold a cerebellum in their research. Instead, they worked with brain scans from a 9.4 Tesla MRI machine, and virtually unfolded and mapped the organ. Custom software was developed for the project, based on the open-source FreeSurfer app developed by Sereno and others. Their model allowed the scientists to unpack the virtual cerebellum down to each individual fold, or "folia."

Study's cross-sections of a folded cerebellum

Image source: Sereno, et al.

A complicated map

Sereno tells SDSU NewsCenter that "Until now we only had crude models of what it looked like. We now have a complete map or surface representation of the cerebellum, much like cities, counties, and states."

That map is a bit surprising, too, in that regions associated with different functions are scattered across the organ in peculiar ways, unlike the cortex where it's all pretty orderly. "You get a little chunk of the lip, next to a chunk of the shoulder or face, like jumbled puzzle pieces," says Sereno. This may have to do with the fact that when the cerebellum is folded, its elements line up differently than they do when the organ is unfolded.

It seems the folded structure of the cerebellum is a configuration that facilitates access to information coming from places all over the body. Sereno says, "Now that we have the first high resolution base map of the human cerebellum, there are many possibilities for researchers to start filling in what is certain to be a complex quilt of inputs, from many different parts of the cerebral cortex in more detail than ever before."

This makes sense if the cerebellum is involved in highly complex, advanced cognitive functions, such as handling language or performing abstract reasoning as scientists suspect. "When you think of the cognition required to write a scientific paper or explain a concept," says Sereno, "you have to pull in information from many different sources. And that's just how the cerebellum is set up."

Bigger and bigger

The study also suggests that the large size of their virtual human cerebellum is likely to be related to the sheer number of tasks with which the organ is involved in the complex human brain. The macaque cerebellum that the team analyzed, for example, amounts to just 30 percent the size of the animal's cortex.

"The fact that [the cerebellum] has such a large surface area speaks to the evolution of distinctively human behaviors and cognition," says Sereno. "It has expanded so much that the folding patterns are very complex."

As the study says, "Rather than coordinating sensory signals to execute expert physical movements, parts of the cerebellum may have been extended in humans to help coordinate fictive 'conceptual movements,' such as rapidly mentally rearranging a movement plan — or, in the fullness of time, perhaps even a mathematical equation."

Sereno concludes, "The 'little brain' is quite the jack of all trades. Mapping the cerebellum will be an interesting new frontier for the next decade."

Economists show how welfare programs can turn a "profit"

What happens if we consider welfare programs as investments?

A homeless man faces Wall Street

Spencer Platt/Getty Images
Politics & Current Affairs
  • A recently published study suggests that some welfare programs more than pay for themselves.
  • It is one of the first major reviews of welfare programs to measure so many by a single metric.
  • The findings will likely inform future welfare reform and encourage debate on how to grade success.
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