Transcriber: Annet Johnson Reviewer: Anna Sobota
In the 1960s, a Nashville insurance executive named Paul Simpson
sat down to watch the evening news.
Among the stories he viewed that night was an interview with Timothy Leary,
the ex-Harvard psychology professor famous for calling on young people
to “turn on, tune in, drop out.”
Simpson couldn’t believe what he had heard that night.
Had someone on national television just called on the youth of America
to take drugs and drop out of school?
Later, Simpson paid a visit to the network headquarters
to see if he could get a copy of the tape,
but no recording was available to be found.
At that time, networks routinely recycled their copies to save on costs.
There was no way for Simpson to verify what he had heard.
Simpson’s frustrating experience forms the origin story
of the Vanderbilt Television News Archive.
The Vanderbilt Television News Archive was started by Paul Simpson
and Frank Grisham, Director of the Libraries,
in August 1968 to record television news
and to make it accessible to researchers, historians, and the public at large.
My colleagues and I continue that mission today,
recording the television news every evening and preserving it for posterity.
But there are new threats to the integrity of media on the horizon
that go far beyond anything our founders could have imagined.
Fast forward in time.
You’re fleeing an enemy force invading your homeland.
An artillery shell has struck the transmission tower
in your neighborhood,
so you can’t get updates from the radio or television.
A fellow refugee tells you
that the president has just put out a video,
calling on the army to lay down its weapons
and for you to return to your home.
Shocked, you pull out your cell phone to search the Internet
and you find a video saying just that.
At least it looks like the president and it sounds like the president,
but you’re just not sure.
What should you do?
Should you press forward or turn back?
What our unfortunate citizen has just seen is a deepfake.
What is a deepfake?
Well, deepfake combines two terms: deep learning,
which is a type of machine learning or artificial intelligence,
with the adjective fake.
As the name implies,
deepfakes transpose the visage and voices of people into artificial settings,
making them say and do things they never actually did.
More broadly, deepfakes belong to the category of synthetic media.
Synthetic media portrays people in situations that never actually happened.
Now, synthetic media has actually been around for a long time.
Digital media artists have created fantastic sequences
that have thrilled moviegoers;
produced by filming actors against green screens
and then painstakingly editing that footage in post-production.
The thing is, it usually takes time and a lot of effort
and a high level of expertise to produce this kind of synthetic media.
In 2014, then doctoral student Ian Goodfellow invented a new technique
called a Generative Adversarial Network or GAN.
Goodfellow’s discovery of GAN sped up the creation of realistic synthetic media.
And researchers around the world have been adapting and refining his technique,
sharing their models publicly on coding sites like GitHub.
The term deepfake first appeared in 2017
when a username “Deepfake” created an eponymous community
on the social networking site Reddit.
That community attracted people interested in the basics of synthetic media
and liking to share their products with others.
When the journalist Samantha Cole caught wind of this community
and wrote about it for the online news network VICE in the same year,
the term deepfake exploded into popular consciousness.
So what motivates someone to create a deepfake?
Well, some people just enjoy the sheer thrill of bending reality a little,
swapping Nicolas Cage into movies that he never starred in
or imagining what Nixon might have said if the lunar lander had crashed on impact.
Concupiscence compels others.
A recent survey shows that the most common reason for making deepfakes
is to take celebrities likenesses and put them into sexually explicit media.
In fact, in 2018, Reddit banned the community r/deepfakes
because of its policy against what it terms involuntary pornography.
But deepfakes have continued to proliferate, nevertheless.
In fact, in March 2022, the worry that experts have long had came to pass
as deepfakes now take the form of synthetic propaganda.
A deepfake of Ukrainian President Vladimir Zelensky appeared
on social networking channels,
calling on his forces to surrender to the Russians.
Now, that deepfake was poorly made and quickly detected,
but it serves as a harbinger of things to come.
What happens when seeing is no longer believing?
How do we establish the truth
when the images and videos around us may be lying?
Now let’s pause to talk about some of the good things
that are coming out of these revolutions in the production of synthetic media.
After all, synthetic media has transported us to distant galaxies
and taken us into alternative dimensions.
Now that those tools are becoming available to the average person,
what good purposes can we put them toward?
Software companies right now are building the underlying machine learning technique
into their products,
and they are allowing us to do amazing things.
For example, you can clone your own voice to make a synthetic podcast,
or you can rotoscope objects automatically out of your videos,
if you don’t like what’s in the background,
or you can dub yourself speaking a language that you don’t even know.
Synthetic media promises to open up
new vistas of creativity for us as individuals.
And as a society, we’ve gotten used to edited video feeds in a way
that would have proved surprising to us just a few years ago.
No, your colleague isn’t calling in to work from Tahiti,
even though the video in her background shows waves rolling over a sandy beach.
“I’m here live, I’m not a cat,” says the mortified attorney.
“I can see that,” says the mollifying judge.
Can we learn to coexist with deepfakes in analogous ways
excepting that appearance in media is never really what it seems?
A problem with taking a laissez-faire approach to deepfakes
is the instinctive credence we lend to the testimony of our senses.
We are psychologically hardwired
to believe the evidence of our eyes and ears.
Now, we can learn to judge appearance more critically.
Philosophers from Plato to the pragmatists
have taught strategies for weighing the evidentiary value
of sensory perception.
Those strategies, though, are hard to learn.
And despite the best efforts of librarians to teach them,
few of us have mastered the requisite critical media literacy.
So what can we do to protect ourselves from the threat of deepfakes?
Well, firms are working on toolkits for detecting synthetic media,
but that's easier said than done.
In the early days of deepfakes,
it used to be that they were glitchy
and you could see that qualities hadn’t transferred properly
from the source video to the target video.
You could look for subtle tells
like fixed unblinking eyes or maybe misshapen ears.
But as progress continues in machine learning,
those tells are becoming fewer and farther between
and, in fact, will soon disappear altogether.
Companies like Sensity.ai and Deepware are making media forensic toolkits
that use algorithms to detect deepfakes.
When they encounter a synthetic video online, they’ll label it as such.
But even the most optimistic technologist doesn’t think
that technology alone will rescue us from the peril of deepfakes.
So what else can we do?
What other measures do we have
to defend ourselves against the threat of deepfakes?
Well, there are legal remedies, but they have their limitations.
Any outright ban of deepfakes would run up against our freedom of expression.
We could limit our attack surface so to speak,
by reducing the amount of media that we consume.
We could switch off our televisions, delete our social media accounts,
and stop doomscrolling from crisis to crisis.
That’s good advice,
especially if you have the fortitude to turn off, tune out, and move on.
As individuals, we should be increasing our digital media literacy
and we should take caution about the velocity at which we consume media.
Just by listening to this talk,
you’ll become more critical about what you hear and see online.
But as a society, we need to do more to protect ourselves.
The solution may come back to trusted authorities.
The philosopher David Chalmers writes in his recent book “Reality+”
that, in the long run, the only way to know for sure
whether an image is real or fake may be authentication by a reliable source.
The Vanderbilt Television News Archive has served as a trusted repository
of the television news for more than 50 years.
As my colleagues and I contemplate our next 50 years,
our mission of safeguarding the news as broadcast, streamed,
or otherwise transmitted remains compelling and vital.
We don’t have all the answers about how to counteract the threat of deepfakes,
but we are ready to rise to the challenge.
In a dawning age of deepfakes,
trusted media repositories like the Vanderbilt Television News Archive
may form our last line of defense
against the unchecked spread of synthetic propaganda.
Thank you.
(Applause)