It’s one of the great information security threats of modern society.
We can have a world leader, Kim Jong-un,
appearing to say an attack is imminent somewhere.
Is it real or is it fake? That's the scary part.
This can be used for anything from
engaging in political influence campaigns...
Jeremy Corbyn to be Prime Minister of our United Kingdom.
to damaging just a private individual’s reputation...
Genuinely love the process of manipulating people online for money.
to making a silly video like superimposing Steve Buscemi’s eyes
on Jennifer Lawrence.
This was very... truly surprising for me.
The techniques I develop
look for things that we call digital fingerprints
or traces that are left behind by any editing.
We’re always engaged in a cat and mouse game
as forensics researchers.
Any time we come up with a way
to detect some sort of image or video manipulation
a new one emerges.
These powerful tools
can be used used by some creative and malicious people
to do some nasty things.
The attacker was able to spoof that voice
and gain access to their corporate systems.
[Super_Users]
[Outpacing Deepfakes]
My name is Matthew Stamm,
and I develop AI algorithms to detect deepfakes and fake images.
Deepfakes are videos where a sophisticated AI algorithm
has learned a model of one person’s face
and then superimposed it on top of another person’s face.
Whoever controls the data controls the future.
Cheapfakes are what we often refer to as fake images or videos
made using commonly available photo and video editors.
So if you Photoshop something,
often we refer to that as a cheapfake,
but it doesn’t mean that the image or video
is any less visually convincing.
The work that I do looks to find evidence of manipulation
to tell us if something’s been falsified,
how it’s been falsified,
where it's been falsified,
and we’re trying to work towards who has falsified it as well.
Then later, we used this to develop algorithms
that can break an image into little tiny parts
and compare the fingerprints of one part of an image
with another part to see if there’s any discrepancies.
So if there’s a discrepancy in the digital traces of an image,
that’s a dead giveaway that the image has been manipulated
or falsified.
Recently, my lab,
in conjunction with another lab at Polytechnic de Milano,
has come up with an algorithm to detect deepfakes
by looking for inconsistent emotion between the face
and the voice track of a video.
At this point in time,
deepfake algorithms that are falsifying a person's face
and voice at the same time
have a hard time of making those emotional expressions match up
in the way that a human would.
One of the first deepfake videos
that really gained national prominence
was a video purportedly of Barack Obama.
How about this? Simply...
President Trump is a total and complete dipshit.
But it was made by the comedian Jordan Peele,
showing people just how good
a deepfake video of a public figure can look.
The problem is, this video looks pretty real.
So sometimes you’ll see
little inconsistencies between when you hear the audio
and when the lips move.
Sometimes the face might appear a little wooden.
Stay woke, bitches.
The first thing I’d look at
is to see if there’s any telltale signs in the video’s metadata.
So metadata are just little tags that are associated with the file
that can tell you things like what camera captured the video
or if it was ever opened in a video editor.
Next, we would use some of the tools that we’ve developed
that would look to see if the forensic traces are consistent.
If you look at the image of the dog without the cast,
this image was created by an anonymous user
in Reddit photoshopbattles,
where they’ve made this poor, injured dog
appear like he’s not injured.
What we've done is we've used some of our forensic analysis algorithms
to identify inconsistent forensic fingerprints inside this image.
So if you look right here, you can see these little green boxes.
These are places that we're using as reference parts of the image
or parts that we’re saying,
“Take a look at the forensic traces here,
and compare them to the rest of the image.”
In red, we’ve highlighted regions
that don’t have consistent forensic traces.
So what that’s saying is
these parts of the image either came from another camera
or have undergone different editing or other processing
than the rest of the image.
We’re explicitly looking at how similar these forensic traces are
between these patches.
So what we’re seeing here is a video of Jim Acosta
speaking in the White House press room.
And this video has been altered to speed up the motion of his hand
to make him look more aggressive.
So this is an example of what people might refer to as a cheapfake.
You’re not using sophisticated AI-driven software
to make this fake video.
You’re just using a common video editor to speed up the video.
And then he had a press conference in the Rose Garden with all this...
So similarly with Nancy Pelosi,
this video was slowed down to make her appear intoxicated.
...currently that he was engaged in a cover-up.
Now, this video actually helped us identify
how to detect speed manipulated videos.
When you speed up or slow down a video,
it leaves behind very distinct fingerprints
that the speed of video has been manipulated.
We can use that to determine if a video has been falsified
through speed manipulation.
This is kind of an overview of how our forensic algorithm works.
And what this does is,
each arm of this network pulls out forensic traces
in these little blocks that are highlighted in red.
Then a later part of the network compares these forensic traces
to produce a measure of how similar or dissimilar
these forensic traces are.
We implemented our own version of the deepfake software, and we--
The very first person to develop a deepfake detection algorithm
was Dr. Siwei Lyu.
...we understand how this thing is generated.
Can we actually take effective defensive measures for that?
He found that deepfakes at that point in time didn’t blink,
so he could detect deepfakes by making a machine learning algorithm
that would track eye movement.
And if in the video, nobody blinked,
it was a dead giveaway that it was a deepfake.
About a week after he published his paper on this,
someone figured out how to make deepfakes blink.
So this shows that
as soon as we come up with ways to detect fake media,
there’s a lot of nefarious actors working on the other end
to try to combat our detection approaches.
I’m doing a Barack Obama, making sure that we--
If we can make an algorithm that can perfectly detect
that an image or video is fake, but it takes five days to run,
by then, the damage is already done.
Someone can influence an election,
someone can already damage the reputation of a public figure,
so we’re always going to be playing catch-up.