Internet companies prepare to fight the 'deepfake' future
Internet
companies prepare to fight the 'deepfake' future
Cade Metz, The New York Times Posted at Nov 25 2019 05:41 AM
SAN
FRANCISCO — Several months ago, Google hired dozens of actors to sit at a
table, stand in a hallway and walk down a street while talking into a video
camera.
Then
the company’s researchers, using a new kind of artificial intelligence
software, swapped the faces of the actors. People who had been walking were
suddenly at a table. The actors who had been in a hallway looked like they were
on a street. Men’s faces were put on women’s bodies. Women’s faces were put on
men’s bodies. In time, the researchers had created hundreds of so-called
deepfake videos.
By
creating these digitally manipulated videos, Google’s scientists believe they
are learning how to spot deepfakes, which researchers and lawmakers worry could
become a new, insidious method for spreading disinformation in the lead-up to
the 2020 presidential election.
For
internet companies like Google, finding the tools to spot deepfakes has gained
urgency. If someone wants to spread a fake video far and wide, Google’s YouTube
or Facebook’s social media platforms would be great places to do it.
Imagine
a fake Sen. Elizabeth Warren, virtually indistinguishable from the real thing,
getting into a fistfight in a doctored video. Or a fake President Donald Trump
doing the same. The technology capable of that trickery is edging closer to
reality.
“Even
with current technology, it is hard for some people to tell what is real and
what is not,” said Subbarao Kambhampati, a professor of computer science at
Arizona State University.
Deepfakes
— a term that generally describes videos doctored with cutting-edge artificial
intelligence — have already challenged our assumptions about what is real and
what is not.
In
recent months, video evidence was at the center of prominent incidents in
Brazil, Gabon in Central Africa and China. Each was colored by the same
question: Is the video real? The Gabonese president, for example, was out of
the country for medical care, and his government released a so-called
proof-of-life video. Opponents claimed it had been faked. Experts call that
confusion “the liar’s dividend.”
“You
can already see a material effect that deepfakes have had,” said Nick Dufour,
one of the Google engineers overseeing the company’s deepfake research. “They
have allowed people to claim that video evidence that would otherwise be very
convincing is a fake.”
For
decades, computer software has allowed people to manipulate photos and videos
or create fake images from scratch. But it has been a slow, painstaking process
usually reserved for experts trained in the vagaries of software like Adobe
Photoshop or After Effects.
Now,
artificial intelligence technologies are streamlining the process, reducing the
cost, time and skill needed to doctor digital images. These AI systems learn on
their own how to build fake images by analyzing thousands of real images. That
means they can handle a portion of the workload that once fell to trained
technicians. And that means people can create far more fake stuff than they
used to.
The
technologies used to create deepfakes are still fairly new, and the results are
often easy to notice. But the technology is evolving. While the tools used to
detect these bogus videos are also evolving, some researchers worry that they
will not be able to keep pace.
Google
recently said that any academic or corporate researcher could download its
collection of synthetic videos and use them to build tools for identifying
deepfakes. The video collection is essentially a syllabus of digital trickery
for computers. By analyzing all of those images, AI systems learn how to watch
for fakes. Facebook recently did something similar, using actors to build fake
videos and then releasing them to outside researchers.
Engineers
at a Canadian company called Dessa, which specializes in artificial
intelligence, recently tested a deepfake detector that was built using Google’s
synthetic videos. It could identify the Google videos with almost perfect
accuracy. But when they tested their detector on deepfake videos plucked from
across the internet, it failed more than 40% of the time.
They
eventually fixed the problem but only after rebuilding their detector with help
from videos found “in the wild,” not created with paid actors — proving that a
detector is only as good as the data used to train it.
Their
tests showed that the fight against deepfakes and other forms of online
disinformation will require nearly constant reinvention. Several hundred
synthetic videos are not enough to solve the problem, because they don’t
necessarily share the characteristics of fake videos being distributed today,
much less in the years to come.
“Unlike
other problems, this one is constantly changing,” said Ragavan Thurairatnam,
Dessa’s founder and head of machine learning.
In
December 2017, someone calling themselves “deepfakes” started using AI
technologies to graft the heads of celebrities onto nude bodies in pornographic
videos. As the practice spread across services like Twitter, Reddit and
PornHub, the term deepfake entered the popular lexicon. Soon, it was synonymous
with any fake video posted to the internet.
The
technology used to create deepfakes has improved at a rate that surprises AI
experts, and there is little reason to believe it will slow. Deepfakes should
benefit from one of the few tech industry axioms that have held up over the
years: Computers always get more powerful, and there is always more data. That
makes the so-called machine-learning software that helps create deepfakes more
effective.
“It
is getting easier, and it will continue to get easier. There is no doubt about
it,” said Matthias Niessner, a professor of computer science at the Technical
University of Munich who is working with Google on its deepfake research. “That
trend will continue for years.”
The
question is: Which side will improve more quickly?
Researchers
like Niessner are working to build systems that can automatically identify and
remove deepfakes. This is the other side of the same coin. Like deepfake
creators, deepfake detectors learn their skills by analyzing images.
Detectors
can also improve by leaps and bounds. But that requires a constant stream of
new data representing the latest deepfake techniques used around the internet,
Niessner and other researchers said. Collecting and sharing the right data can
be difficult. Relevant examples are scarce, and for privacy and copyright
reasons, companies cannot always share data with outside researchers.
Although
activists and artists occasionally release deepfakes as a way of showing how
these videos could shift the political discourse online, these techniques are
not widely used to spread disinformation. They are mostly used to spread humor
or fake pornography, according to Facebook, Google and others who track the
progress of deepfakes.
Right
now, deepfake videos have subtle imperfections that can be readily detected by
automated systems, if not by the naked eye. But some researchers argue that the
improved technology will be powerful enough to create fake images without these
tiny defects. Companies like Google and Facebook hope they will have reliable
detectors in place before that happens.
“In
the short term, detection will be reasonably effective,” said Kambhampati, the
Arizona State professor. “In the longer term, I think it will be impossible to
distinguish between the real pictures and the fake pictures.”
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