Americans have been programmed to love to be entertained. Even our
news is packaged in fluffy bows and shiny wrapping paper. But here's
a look down the Rabbit Hole, where the rabbits all talk...or seem to
talk...or do they?
But who better to ask than the Master of Fake Reality...Donald Trump...really!
article
-Well, it's not going to get
turned around in 10 years.
The Internet is filled with false or misleading videos. The Post’s
Fact Checker built a guide to help you spot the manipulation. (Elyse
Samuels/The Washington
Post)
By
Drew Harwell
June 12
Top artificial-intelligence researchers across the country are racing
to defuse an extraordinary political weapon: computer-generated fake
videos that
could undermine candidates and mislead voters during the 2020
presidential campaign.
And they have a message: We’re not ready.
The researchers have designed automatic systems that can analyze
videos for the telltale indicators of a fake, assessing light,
shadows, blinking patterns
— and, in one potentially groundbreaking method, even how a
candidate’s real-world facial movements — such as the angle they tilt
their head when they
smile — relate to one another.
But for all that progress, the researchers say they remain vastly
overwhelmed by a technology they fear could herald a damaging new wave
of disinformation
campaigns, much in the same way fake news stories and deceptive
Facebook groups were deployed to influence public opinion during the
2016 election.
Powerful new AI software has effectively democratized the creation of
convincing “deepfake” videos, making it easier than ever to fabricate
someone appearing
to say or do something they didn’t really do, from
harmless satires and film tweaks
to
targeted harassment and deepfake porn.
And researchers fear it’s only a matter of time before the videos are
deployed for maximum damage — to sow confusion, fuel doubt or
undermine an opponent,
potentially on the eve of a White House vote.
“We are outgunned,” said Hany Farid, a computer-science professor and
digital-forensics expert at the University of California at Berkeley.
“The number
of people working on the video-synthesis side, as opposed to the
detector side, is 100 to 1.”
[Facebook wouldn’t delete an altered video of Nancy Pelosi. What about
one of Mark Zuckerberg?]
ADVERTISING
These AI-generated videos have yet to drive their own political
scandal in the United States. But even simple tweaks to existing
videos can create turmoil,
as happened with the recent viral spread of a video of House Speaker
Nancy Pelosi (D-Calif.), distorted to make her speech stunted and
slurred. That video
was viewed more than 3 million times.
Deepfakes have already made their appearance elsewhere: In Central
Africa last year, a
video
of Gabon’s long-unseen president Ali Bongo, who was believed in poor
health or already dead, was decried as a deepfake by his political
opponents and
cited as the trigger, a week later, for an unsuccessful coup by the
Gabonese military.
And in Malaysia, a viral clip of a man’s
seeming confession
to having sex with a local cabinet minister is being questioned as a
potential deepfake. He “does not look like this. . . . His body isn’t
as built as
in the video,” a local politician said, according to
the Malay Mail newspaper
in Kuala Lumpur.
The threat of deepfakes, named for the “deep learning” AI techniques
used to create them, has become a personal one on Capitol Hill, where
lawmakers believe
the videos could threaten national security, the voting process — and,
potentially, their reputations. The House Intelligence Committee will
hold a hearing Thursday
in which AI experts are expected to discuss how deepfakes could evade
detection and leave an “enduring psychological impact.”
Rep. Adam B. Schiff (D-Calif.), who chairs the committee, said
Thursday, “I don’t think we’re well prepared at all. And I don’t think
the public is aware
of what’s coming."
public/MPRLRKUNJQI6TNXUAMZVMUBNZY
An image created from a fake video of former president Barack Obama
displays elements of facial mapping used in new technology that allows
users to create
convincing fabricated footage of real people, known as “deepfakes.” (AP)
Rachel Thomas, the co-founder of Fast.ai, a machine-learning lab in
San Francisco, says a disinformation campaign using deepfake videos
probably would
catch fire because of the reward structure of the modern Web, in which
shocking material drives bigger audiences — and can spread further and
faster than
the truth.
“Fakes often, particularly now, don’t have to be that compelling to
still have an impact," Thomas said. “We are these social creatures
that end up going
with the crowd into seeing what the other people are seeing. It would
not be that hard for a bad actor to have that kind of influence on
public conversation.”
No law regulates deepfakes, though some legal and technical experts
have recommended adapting current laws covering libel, defamation,
identity fraud or
impersonating a government official. But concerns of overregulation
abound: The dividing line between a parody protected by the First
Amendment and deepfake
political propaganda may not always be clear-cut.
And some worry that the potential hype or hysteria of fake videos
could even erode how people accept video evidence. Misinformation
researcher Aviv Ovadya
calls this problem “reality apathy”: “It’s too much effort to figure
out what’s real and what’s not, so you’re more willing to just go with
whatever your
previous affiliations are.”
[Fake-porn videos are being weaponized to harass and humiliate women:
‘Everybody is a potential target’]
It might already be leaving an impact. In a
Pew Research study
released this month, about two-thirds of Americans surveyed said
altered videos and images had become a major problem for understanding
the basic facts
of current events. More than a third said “made-up news” had led them
to reduce the amount of news they get overall.
There also are fears that deepfakes could lead to people denying
legitimate videos — a phenomenon the law professors Robert Chesney and
Danielle Citron
call “the liar’s dividend.” President Trump, for instance, has told
people the “Access Hollywood” video, in which he boasted of assaulting
women, was
doctored.
(After the real audio was first revealed by The Washington Post in
October 2016, Trump apologized for the remarks.)
Officials with the Democratic and Republican parties and the nation’s
top presidential campaigns say they can do little in advance to
prepare for the damage,
and are counting on social networks and video sites to find and remove
the worst fakes. But the tech companies have differing policies on
takedowns, and
most don’t require that uploaded videos must be true.
“People can duplicate me speaking and saying anything … and it’s a
complete fabrication,” former president Barack Obama told an audience
in Canada last
month. “The marketplace of ideas that is the basis of our democratic
practice has difficulty working if we don’t have some common baseline
of what’s true
and what’s not.”
The technology is progressing rapidly. AI researchers at the Skolkovo
Institute of Science and Technology in Moscow last month
unveiled a “few-shot” AI system
that could create a convincing fake of someone with only a few still
photos of their face. The lead researcher, Egor Zakharov, said he
could not discuss
it, citing ongoing peer review, but in a statement the team said that
the “net effect” of making video special-effects technologies more
widely available
“has been positive … [and] we believe that the case of neural avatar
technology will be no different.”
[Faked Pelosi videos, slowed to make her appear drunk, spread across
social media]
Another group of AI researchers, including from Stanford and Princeton
universities, just
debuted a separate system
that can edit what someone appears to be saying on video, just by
changing some text, with the AI swapping around the person’s voiced
syllables and mouth
movement to leave only a seamlessly altered “talking head.”
The lead researcher, Ohad Fried, said the technology could be used to
enhance low-budget filmmaking and help localize videos to
international languages
and audiences. But he also said it could be abused to falsify video or
“slander prominent individuals.” Video made using the tool, he said,
should be presented
as synthetic. But he said regulators, tech companies and journalists
should play a more leading role in researching how to unmask fakes.
“In general people do need to understand that video may not be an
accurate representation of what happened,” he said.
Deepfake video is just one part of how AI is revolutionizing
disinformation. New natural-language AI systems such as
GPT-2,
by the research lab OpenAI, can feed on written text and spit out many
more paragraphs in a similar tone, theme and style — a boon, perhaps,
to spam chatbots
and “fake news” creators, even if the underlying ideas sometimes trend
toward gibberish.
The technique has
already been used
to automatically parrot political leaders’ speaking style after
“learning” from hours of U.N. speeches. To counteract it, researchers
at the University
of Washington and the Allen Institute for Artificial Intelligence last month
unveiled a fake-text-detector system,
called Grover, that could potentially expose what it calls
machine-generated “neural fake news.”
Convincing fake audio is also on the horizon, including from
Facebook AI researchers,
who have replicated a person’s voice using computer-generated speech
that sounds deceivingly lifelike. The system, MelNet, learned its
impersonations by
listening to hundreds of hours of TED Talks and audiobooks;
in samples,
the system can make Bill Gates, Jane Goodall and others say sentences
such as “A cramp is no small danger on a swim.”
[Pelosi says altered videos show Facebook leaders were ‘willing
enablers’ of Russian election interference]
In AI circles, identifying fake media has long received less
attention, funding and institutional backing than creating it: Why
sniff out other people’s
fantasy creations when you can design your own? “There’s no money to
be made out of detecting these things,” said Nasir Memon, a professor
of computer
science and engineering at New York University.
Much of the funding for researching ways of detecting deepfakes comes
from the Defense Advanced Research Projects Agency, the Pentagon’s
high-tech research
arm, which in 2016 launched a “Media Forensics” program that sponsored
more than a dozen academic and corporate groups pursuing high-level
research. Matt
Turek, a computer-vision expert who leads the DARPA program, called
synthetic-media detection a “defensive technology” against not just
foreign adversaries
but domestic political antagonists and Internet trolls.
“Nation-states have had the ability to manipulate media since,
essentially, the beginning of media,” Turek said. But a strong-enough
fake-spotting system
would make it so groups with more-limited resources would face “enough
computational burden to make it not worth the risk.”
The trick for unraveling a deepfake, researchers said, is building a
tool that works in what cryptography circles call a “trustless
environment,” in which
authoritative details of the video’s creator, origin and distribution
can be impossible to trace. And speed is critical: With every minute
that an investigator
spends debunking video, a clip can spread that much further across the Web.
[Scarlett Johansson on fake AI-generated sex videos: ‘Nothing can stop
someone from cutting and pasting my image’]
Forensic researchers have homed in on a range of subtle indicators
that could serve as giveaways, such as the shape of light and shadows,
the angles and
blurring of facial features, or the softness and weight of clothing
and hair. But in some cases, a trained video editor can go through the
fake to smooth
out possible errors, making it that much harder to assess.
With one new method, researchers at the universities of California at
Berkeley and Southern California built a detective AI system that they
fed hours
of video of high-level leaders and trained it to look for
hyper-precise “facial action units” — data points of their facial
movements, tics and expressions,
including when they raise their upper lips and how their heads rotate
when they frown.
To test these “soft biometric” models, Farid and his team worked with
a team of digital-avatar designers to create some deepfakes of their
own, swapping
the faces of Sen. Elizabeth Warren (D-Mass.), Hillary Clinton and
President Trump onto their own impersonators on “Saturday Night Live.”
The system has
scored high in accuracy on gauging a number of different kinds of
fakes: videos of a satirical human impersonator; “face-swap” fakes,
popular in social-media
apps; “lip-sync” fakes, in which the real face remains but the mouth
is substituted; and “puppet-master” fakes, in which a target’s face is
placed onto
an actor’s body.
The research, titled “Protecting World Leaders Against Deep Fakes,”
was partially developed with funding from Google, Microsoft and DARPA.
It will be revealed
alongside other techniques next week in California at the Conference
on Computer Vision and Pattern Recognition, a landmark annual summit
sponsored
by the biggest names in American and Chinese AI.
public/C4IG5AENLAI6TNXUAMZVMUBNZY
Digital-forensics expert Hany Farid speaks during a forum on
deepfakes. (Maury Phillips/Getty Images for SAG-AFTRA)
Sam Gregory, a program director at
Witness,
a human-rights group that helps train amateur journalists around the
world to record abuse, said the world’s social media platforms need to
unify around
a “shared immune system” designed to find and stop viral fakes.
Scanning top politicians’ faces using Farid’s method, Gregory said,
would offer protection
to high-level leaders, but not to local politicians, journalists or
other people who could be vulnerable to attack.
Farid wants media outlets to have access to the deepfake-detecting
tool so they can assess news-making video when it arises. But making
the system more
widely available carries its own threat, by potentially allowing
deepfake creators to examine the code and find workarounds. This
cat-and-mouse game is
a long-running frustration for forensic researchers, ensuring that
even a promising detection method is only of temporary use.
Siwei Lyu, director of a computer-vision lab at the State University
of New York at Albany, helped pioneer research last year that found
many deepfakes
had a telltale clue: a lack of blinking. It was an investigative
victory — until two weeks later, when Lyu received an email from a
deepfake creator who
said they had solved the problem in their latest fakes.
Lyu believes media manipulation can have a broader psychological
effect, by subtly shifting people’s understandings of politicians,
events and ideas.
“Everybody knows it’s a fake video. But they watch it,” Lyu said.
“It’s generating an illusion. It can wreak a lot of damage. It’s very
hard to remove.
And it can come from anywhere. With the Internet, all the boundaries
are becoming blurred.”
[Fake news is about to get so much more dangerous]
High-definition fake videos often are the easiest to detect,
researchers said. The more detail in a video, the more opportunities
for the fake to reveal
its flaws. But the modern Web works against that advantage because
most social media and messaging sites compress the videos into formats
that make them
quicker and easier to share, removing critical clues.
That challenge to some appears insurmountable, and has led some
researchers to instead pursue an authentication system that would
fingerprint footage right
as it’s captured. It could help make fakes easier to spot, but would
require agreement from makers of smartphones, cameras and websites — a
far-off proposal
that could take years.
“I worked on detection for 15 years. It doesn’t work,” Memon said.
“Facebook videos? Things thrown around in WhatsApp? . . . It may never
work. Meanwhile,
the adversary has really gone up a few notches.”
Political campaigns that have long prepared defenses against bruising
video gaffes said they were stumped on how to prepare for the new
weapons of mass
deceptions. Several campaign officials said they pinned their hopes on
the tech companies acting more aggressively to police for fakes.
A Democratic National Committee official said it has helped train
campaigns on how to combat disinformation and push for takedowns from
the social-media
sites. A Republican National Committee official said it is encouraging
employees to stay on alert for suspicious content, and that its
digital team works
with the tech giants to flag harmful posts and accounts.
[A reason to despair about the digital future: Deepfakes]
But the tech giants’ policies don’t align on whether fakes should be
deleted or flagged, demoted and preserved. YouTube, for instance,
quickly pulled the
distorted Pelosi video, saying it violated its “deceptive practices”
policies. But Facebook kept it online, saying in a statement to The
Post that “we
don’t have a policy that stipulates that the information you post on
Facebook must be true.”
YouTube said it is “exploring and investing in ways to address
synthetic media” and compared it to previous challenges, such as
fighting spam and finding
copyright-infringing videos, that it has tackled with a mix of
software and human review.
Facebook is funding some universities’ manipulated-media research and,
in a statement to The Post, said “combating misinformation is one of
the most important
things we can do.” The company was targeted by its own fake this week,
when an altered video of chief executive Mark Zuckerberg appeared to
show him boasting
of his “total control” over the world’s data. (The fake remains online.)
Twitter said it challenges more than 8 million accounts a week that
attempt to spread content through “manipulative tactics." But
fact-checking every tweet
is not feasible, the company said, adding that it doesn’t “think we
should set the precedent of intervening to decide what is and is not
truthful online.”
[The Technology 202: Doctored Pelosi video is leading tip in coming
disinformation battle]
The company added that “the clarification of falsehoods happens in
seconds” on the site because of real-time checks from other users, and
that “typically
factually inaccurate material gains very little distribution on
Twitter until it is" disproved. The company could not offer any
statistics to support that
claim.
Perhaps the most pervasive problem for modern visual storytelling,
researchers said, is not sophisticated fake videos but misattributed
real ones: footage
of a real protest march or violent skirmish, for instance, captioned
as if it had happened somewhere else.
The detection systems have taken on a newfound urgency due to the
upcoming election, but there is also a growing interest from corporate
America to protect
against viral frauds. Shamir Allibhai, the founder of Amber, a small
fake-detection start-up, said his firm is working now with a test
group of corporate
clients seeking a shield against deepfakes that could show, for
instance, a chief executive saying racist or misogynistic slurs.
In a world where video has played a pivotal role in shaping modern
history, researchers said it’s nevertheless critical to find a way to
spot the fakes
— and some fear what could happen if the authority of video slips away.
“As a consequence of this, even truth will not be believed,” Memon
said. “The man in front of the tank at Tiananmen Square moved the
world. Nixon on the
phone cost him his presidency. Images of horror from concentration
camps finally moved us into action. If the notion of not believing
what you see is under
attack, that is a huge problem. One has to restore truth in seeing aga
