November 20, 2018 • 35 mins
Today, it's possible to manipulate photos and videos in ways that were unthinkable just a few years ago. We look at a research project that uses computers and AI to map the movements of professional dancers to the images of people who have two left feet.
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Episode Transcript
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Speaker 1 (00:04):
Get in touch with technology with tech Stuff from how
stuff works dot com. Hey there, and welcome to tech Stuff.
I'm your host, Jonathan Strickland. I'm an executive producer and
I love all things tech. And here's a fun fact
about getting older. As you age, stuff that you once
(00:25):
thought was impossible will become not only possible, but will
become the norm, and future generations won't even think about
what it must have been like before the impossible was commonplace.
Now this is true for every generation. It's not like
this is, you know, a brand new, groundbreaking observation. Plenty
(00:45):
of people have made it before me, but I want
to talk about a specific implementation. For example, with photography,
it used to be pretty difficult to manipulate pictures convincingly.
There have been photo and tools for decades, but generally
it took a great deal of skill and training, plus
(01:07):
access to specialized equipment to pull it off, especially in
the old film days. Now, gradually, tools like Photoshop made
it easier to manipulate digital images. Now it still requires
a certain level of skill to pull off a really
convincing job, and it's easy to make really badly manipulated images.
But as these tools became widely available, people began to
(01:30):
learn how to use them. We had to come to
the realization that we cannot necessarily believe our own eyes
when we're looking at a digital image. Now, the same
thing is happening with video footage. It's quite possible to
fake video footage, though again, if you want to do
it really well, it requires some skill, some specialized tools,
(01:52):
and to really get some expertise in it in order
to do it in a way that's really convincing. But
it's a pretty recent techn logical capability in fiction, however,
it's been around for a long time. I remember seeing
the movie Rising Sun back in the early nineteen nineties. Now,
in that film, Wesley Snipes plays a detective and Sean
(02:13):
Connery in his most convincing roles, since he played an
Egyptian immortal posing as a Spaniard with a Scottish accent
in Highlander, would play a Japanese customs and culture expert
Sean Connery. Anyway, the film is a mystery thriller, and
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while the two are investigating a murder, they come into
possession of some video footage, and they find out that
video footage has actually been manipulated it was planted for
them to find so that would put them on the
wrong trail. One person's face was replaced with someone else's,
and in a somewhat comedic scene, the video editor who
(02:55):
is explaining this casually swaps the heads of Snipes and
Connor in real time in a video feed to show
off this capability, which might have been a tad unrealistic,
but today we are in a world in which video
manipulation of increasingly convincing quality is achievable in real time.
In fact, these days, it's possible to use sophisticated computer
(03:17):
algorithms to allow for manipulation of captured video, almost as
if the video was a computer generated cartoon reacting to
real time inputs like a video game controller, only instead
of it being a video game, it's a real person
on video. There are, of course, lots of ways this
technology could be used unethically, and one of the best
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known has been the focus of this whole conversation around
video manipulation, and it comes from a former Reddit user
who went by the handle deep fakes. Now that handle
has become the shorthand for the general practice, which frequently,
but not exclusively, would involve replace the face of an
(04:01):
actor in a pornographic scene with someone else's face like
that of a celebrity, which is pretty darn unethical and creepy.
The name itself was a reference to the technology used
in the approach, so it relies on a process called
deep learning. Deep learning is a type of machine learning.
(04:22):
It's a sub type of machine learning that utilizes artificial
neural networks. And I've talked an awful lot about those
kind of networks recently, so I'm not going to go
over the whole thing again. Will give just a really
quick rundown to say, you have nodes, artificial neurons in
these networks that receive input from potentially multiple other nodes,
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and then on that input, your artificial neuron that you're
looking at will perform some sort of weighted operation and
produce a single output. That single output can move on
to become one of many inputs for a different ARTIFICI
shoal neuron in that network, and so on and so forth.
Deep learning networks are very very large artificial neural networks,
(05:08):
and they can accept a large amount of training data.
This is a scalable approach. This means the larger the
network and the more data you can feed it, the
better it performs. This is different from any other machine
learning models. Those tend to hit a performance plateau once
you hit a certain size, which means that if you
were to add more nodes to the network, you wouldn't
(05:32):
necessarily see a comparative increase in performance. You would you
would kind of flatten out over time. In fourteen, a
deep learning expert named Andrew ng gave a talk at
Stanford about the best use cases for deep learning, and
he mentioned that it was particularly good at supervised learning tasks. Now,
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these are the types of computer problems in which we
humans already know the answer, such as is there a
cat in this photograph? Humans can pick up on that
right away, assuming someone has not carefully hidden a cat
in a very busy image. But for a computer, this
is a much more difficult problem. Even if the picture
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has a cat center stage, it can be tough for
a computer to figure that out using a supervised learning approach.
With a deep learning network, you can train a system
to recognize cats and images with a high degree of
success if you have a large amount of training data
to train the network to recognize cats. Now, in this
(06:36):
other case, that I was talking about. The Reddit user
called deep fakes started posting on Reddit in late twenties seventeen.
The user made an open source code version of a
deep learning algorithm and made it available for the purposes
of video manipulation and anyone could take advantage of it. Now. Specifically,
this algorithm was designed for face swapping. The algorithm would
(07:00):
allow you to put the face of one person onto
the body of another in video form, and it wasn't
always convincing. In fact, it could often be easily detectable
as fake if someone had not trained the model properly
before creating the video. But it did open up a
can of worms once the practice started getting media coverage. However,
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the actual technology to pull this off was already a
couple of years old when deep fakes shared it. Back
in there was a group of researchers from Stanford then
and also the University of Erlanger Nuremberg and the Max
Planck Institute for Informatics who collectively published a paper that
was titled Face to Face Real Time Face Capture and
(07:45):
Reenactment of r GB Videos and that's a face, the
number two and face. The paper details the methodology the
group used to create a pretty incredible effect. The algorithm
could take the facial expressis from one person and transfer
them in real time to a video target. It was
(08:06):
like turning the video into a digital puppet. So you
might have a video loop of a celebrity running, and
preferably it's a loop that's easily repeatable without the repeat
being terribly noticeable, and that's your target video. So if
you just let it run, you just would see video
of someone sitting down, maybe looking around a little bit,
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but that's it, nothing special. Then you would have a
source subject sitting in view of a consumer quality webcam,
no special equipment here, and that person could make different expressions,
including opening and closing their mouths, and the video target
would match them move for move, like a digital puppet. Moreover,
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the source subject didn't have to wear any special gear.
They didn't have to have any special markers, none of
those dots that you would see with motion capture. None
of that was necessary. All the algorithm needed was a
video feed from a monocular camera, so you didn't even
need depth perception for this. There's a video of their
work on YouTube that shows off this process and includes
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a loop of George W. Bush sitting for an interview.
The source subject can manipulate the face that Bush makes
just by making faces of his own, and the algorithm
would map those movements to the target video. And it's
pretty wild to see an image of moving image of
George W. Bush responding in real time to all of
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these different facial expressions this guy is making. So how
did the team do this? It's one thing to say
a deep learning algorithm gave them this capability, but that's
not really an explanation. The paper definitively spells this out
in real technical detail. It starts off the explanation by saying, quote,
in our method, we first reconstruct the shape identity of
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the target actor using a new global non rigid model
BA fast bundling approach based on a prerecorded training sequence.
As this pre process is performed globally on a set
of training frames, we can resolve geometric ambiguities common to
binocular reconstruction. At runtime. We tracked both the expressions of
the source and target actors video by a dense analysis
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by synthesis approach based on a statistical facial prior end quote,
and it goes on in that vein throughout the paper
which means it gets pretty dense. But I think we
can suss out what's going on from a high level
if we just take a moment. But first, I'm going
to take a moment of my own to thank my sponsor.
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So how did the Face to Face team build this tool? Well,
for each target video, they would collect a large sample
of footage and images and feed it to this deep
learning algorithm. This would be necess sarry to identify all
the points on the face that would move with various expressions,
as well as to capture images of the inside of
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the target's mouth when he or she spoke. This is
because the video loop they used to create the manipulated
video would feature the target subject, typically with his or
her mouth closed, so it might be a section in
which the subject was sitting down for an interview and
listening to an interviewer's questions but not responding yet they
were just listening. The additional video would provide information about
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the inside of the target subject's mouth, which could be
rendered in time on the target video performance when it
came time to do that. Their approach improved the scanning
technique to build face templates for both the source subject
who provides all the expressions and the target subject, who
mimics all the expressions. As the source subject makes different
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facial expressions, the computer face template detects how the subjects
face changes or deforms over time. The computer model then
takes that information, saying, all right, well the lips moved
in this way, there was a grimace here or a
smile there, and transfer those motions to the targets face template,
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which is matched to the target's actual face. This process
transfers the expressions over to the target, and so when
the source subject grimaces, the target grimaces, if the source
subject just it's still the target. Video will continue to
loop and the targets face won't change. The more video
footage you can get of your target and your source,
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the better the computer algorithms are that create those face templates,
and the more natural the manipulation will appear on the
finished video. You also want a really good amount of
footage just to get all that extra information you need,
like the inside of the mouth, so that that can
all be extrapolated properly. You have to design a tool
that can encode an image called the training image, and
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then decodes this data to reconstruct the image. So imagine
you've got a picture. The encoder essentially creates data based
on that image. It's like a description of that image.
The decoder takes the description and tries to rebuild the
image based on the description. I think of this like
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that scene in Willy Wonka where Mike TV gets broken
up in a million little pieces and then gets reconstructed
on the television screen. So the second image is not
a copy. It's not like you made a copy of
the first one. It's like you built a new image
based on the first one. By the way, when you
start off with these uh these algorithms, those reconstructions tend
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to look pretty bad. You have to continually train and
train and train and train the model so that it
gets better and better at producing a close representation of
the original image. When it does, it's reconstru struction. And
you would have both essentially decoders for both your source
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subject and your target subject. Use the same encoder for both,
but two different decoders, one dedicated to your source, one
dedicated to your target. Then you would feed the reconstructed
images through the system again and again. This is called
back propagation. You do this over millions of times, typically
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to improve this process, and then you're ready to really
switch switch things up. So let's say we've got two people.
We've got person one and we've got person two, and
you've been feeding images of both of these people through
the same encoder, but of course you have dedicated decoders
to produce the reconstruction. So person one has decode er
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one and person two has decoder two. Now let's say
you're ready to put person two's face on person one's body. Well,
you would feed an image of person one into the encoder,
but you use the decoder for a person to to
reconstruct the image, and what you get is persons who's
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face but mimicking the expression from person one. You, or
rather the computer algorithm, does this frame by frame on video,
and you end up with a video appearing to feature
one person when in fact it's just their face on
top of someone else, and it's their face making the
exact same expressions as whoever was originally in that video.
(15:32):
Now back over to deep fakes. Before long after the
Reddit user initially posted this code, folks over at Reddit,
we're taking this open source code and making more advanced
software based off of it. Soon there were desktop apps
that would take over all the hard parts of this process,
all the codey bits, if you will, of training a model.
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Some of them would guide users into creating the data
that would be used to train the model and go
all the way through the process of creating the final
fake videos. Even with some of the more sophisticated versions,
there were tell tales signs of tampering. Typically some blurring
around images, particularly near chins and mouths. Those would be signs.
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If there was any flicker, that was a sign if
you didn't take enough time to train the model. Typically
you would want to do several days of training at least.
If you didn't take that time, you might see some
really nasty blurring and flickering, and it would be a
dead giveaway that this was tampered. Video in writer, director,
(16:35):
and comedian Jordan's Peel demonstrated the power of this technology.
He showed how, with his impersonation of Barack Obama and
some manipulation software, he could create a fake public service
address when which the president would appear to say things
that he normally would never say. The technology behind this
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made use of what is called a long short term
memory network or l s TM, to go into the
mechanics of that would require another podcast, but using an
approach similar to what I've already described, a team was
able to make a video of Obama apparently lip syncing
Peel's satirical message. The goal of this p s A
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was beyond alert because fakes are getting harder to spot.
The University of Washington showed off this and They're Synthesizing
Obama project in which they took the audio from one
of President Obama's speeches and then used it to animate
his face in video from a different address that he
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gave during his presidency. So in this example, the person
in the target video is the same person as the
source for the audio. But the point was pretty clear
that tech would soon make it possible to fake someone
saying or doing something. It just takes the right algorithms,
the right amount of training data, and the right amount
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of time to get the model trained up enough to
do it smoothly. Now, this technology could be used to
do stuff that isn't related to malicious deception or for
pornography or anything along those lines. It could be used
in television and film for lots of stuff, including potentially
adding in actors who have passed away into a film.
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Paired with similar work that's going on in voice synthesis,
you could end up with a convincing replacement, which means
we could make movies with dead actors taking on new
parts because we can synthesize their speech, we can synthesize
their appearance. You would still have someone else acting out
the part physically, but you would replace their image with
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this actor's image. Or maybe you would want to use
this kind of technology just to make everyone think you
can cut a rug. This brings me to the University
of California, Berkeley and is the subject of a paper
titled Everybody Dance Now. The goal is a simple concept
that's actually really hard to pull off. What if you
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were to take the movements of a professional dancer and
then map those movements onto the body of someone who
wasn't a dancer. What if you could create a video
in which literally anyone would appear to move like a skilled,
trained dancer. And how the heck would that be possible. Well,
at the heart of the team's efforts was something I
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talked about in a recent episode of tech Stuff about
an AI generated portrait, and that would be generative adversarial
networks or g A n s. These use a pair
of artificial neural networks in competition against each other. So
since I covered this recently, i'll just give again a
super quick high level summary. You've got one network that
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has a specific job, such as trying to create an
original image of a cat. We'll go back to the
cat pictures. That's one of my favorite ones because it
was one of the early use cases of neural networks
that I remember encountering when I was doing research. Now,
let's say you've got your second network. Your second network
has the specific job of evaluating pictures of cats to
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determine if they are valid, meaning is this a real
picture of a cat that's part of the training material
that I'm accepting, or is this, in fact a fake
that was created by a computer program the other neural network.
So you've got one network trying to fool the other network.
And these networks get better at what they do over time,
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they improve, So your counterfeit network is getting better and
better at making fake pictures of cats, and your detector
network is getting better and better at detecting fake images
of cats. Now, typically this requires humans to give feedback
or tweaking weight values along the networks, but they do
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get better over time. So if the network trying to
create a picture of a cat gets the feedback of sorry, buddy,
but they're onto you, then it can try again and
adjust it's approach slightly in an effort to fool the
second network. If the second network gets the feedback you'll
let this one slip by and it's fake, then it
will adjust or it will be adjusted to look out
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for any tailtale signs that it had missed in that
earlier evaluation. Over time, the two networks working against each
other will create the ultimate result of better and better
computer generated content, whether it's an image of a cat
or a sonnet, or a song or a video. Now
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that doesn't mean that these computer generated things are at
the same level as human generated stuff, especially when it
comes to text. I've seen a lot of song lyrics
that were inscrutable even by my old man standards. So
I think that we're a long way away from getting
to a point where they can fool us in every case.
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But with video they're getting pretty darn good. Now, this
team had two groups of subjects, and so you had
your source subjects and your target subjects. The source in
this case, were the people who could dance, so like
ballet dancers, hip hop dancers and that sort of stuff.
They legit know how to move. They would demonstrate various
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dances on video. The second group of subjects were your
target subjects. These were not trained dancers. They were to
go through a series of moves and poses, essentially aping
as best they could the movements of trained dancers, and
the goal of this pair of networks was to smooth
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the movements out and adjust the timing so that these
untrained dancers would appear to move more like their groovy
source subject counterparts. I'll explain more in just a moment,
but first let's take another quick break to thank our sponsor.
(23:01):
According to the Everybody Dance Now paper, the team would
transfer motion between the sources to the target through an
end to end pixel based pipeline. So here's how that's done.
Because if you're like me, that phrase meant next to
nothing to you. So specifically, the group used three stages
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to take the movements of one person and transpose them
to a target person. Those three were pose detection, global
pose normalization, and mapping from normalized pose stick figures to
the target subject. Post detection involves teaching machines, in other words,
computers how to interpret images to determine where key body
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points are, like elbows, knees, hips, shoulders, the head, that
kind of stuff. That first requires that you teach the
machine to recognize those points in the first place. So
first you have to train a machine to recogniz eyes
those points and identify them with a target level of accuracy.
It's pretty typical to represent these joints as as points
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in a stick figure, so each point represents another joint
or point of articulation. The lines represent the trunk of
the body, the limbs, the head. You end up with
a stick figure. If your machine learning mechanism was a
good one, the machine should be able to overlay a
stick figure on top of any image of a person posing,
and the stick figure should more or less conform to
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that image, including where the actual joints are. So if
you have someone standing there in the classic Peter Pan
pose of their their fists on their hips uh and
their their arms out of kimbo, then it should draw
a stick figure that's essentially aping the same thing and
be able to overlay it on top of the original image.
Now these days this can be done in real time. So,
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for example, there's a team at Google Creative Lab that
used a machine learning model of pose net and created
a JavaScript version with TensorFlow, which is an open source
software library often used for machine learning. And with this
tool you can do real time pose estimation through a
browser and a webcam. The application doesn't have any technology
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related to identifying the person in the image. It's just
quote unquote interested in what the person is doing, not
who the person is. So you can actually run this
on your own machine in a browser, and you can
pose in front of a webcam and you'll see the
little stick figure uh painted on top of your image
on the computer. Essentially, so every time you move, every
time you bend a joint, you will see the stick
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figure doing the same thing, um mapped on top of you.
The Berkeley team made use of a pre trained pose detector,
meaning they didn't build a new one, which helps save
a lot of time and expense on their project. Now
people come in all shapes and sizes. In the video
the team released, they showed off subjects who included a
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woman who appeared to be of around average height and
a man who appeared to be pretty darn Tallman transfer
method that would only work between a subject and a
target who are of similar shape and size would be
pretty limited. So the purpose of the global pose normalization
stage is to account for all the differences between the
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source and the target subjects and the locations within the
frame of the camera. Without this step, the motion transfer
might appear ghoulish. We don't have all the same proportions, right,
so a mismatch might mean a target's limbs would appear
to bend in places that were clearly not natural joints.
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All you need to do is see an arm bend
where an arm isn't supposed to bend, and that's going
to ski the out quite a bit. Makes an effective
horror movie experience, but not one that would produce convincing
motion transfer. Now, there are a lot of ways that
the team could have gone about normalizing the poses, but
their choice seems particularly clever to me. They measured the
heights and ankle positions of the various subjects and used
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linear mapping between the closest and farthest ankle positions in
both videos to normalize the stick figure for the target subjects.
The program would calculate the scale of the figure as
well as the scale of motion from frame to frame.
And I think that's pretty darn cool because it wasn't
just accounting for the size of the subjects to get
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all the joints right, but also to make sure the
scale of the movements with respect to the body size
and proportions would remain the same. So a tall person
with really long limbs moving their arms in really big, big,
bold gestures, if you tried to transfer that motion to
someone who was of smaller stature, it could really look disturbing.
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But by using this scaling approach, the movements on the
smaller person would be proportionate in size to the movements
of the larger person. The team would use two of
the Generative Adversarial Network setups to work on making a
convincing final video. The first was dedicated to image to
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image translation, attempting to manipulate the image of the target
subjects that would follow the motions made from the pose
detection process, and like all g a N setups, this
included the generator, which would attempt to create a convincing
sequence of images, and the discriminators, which tried to weed
out the quote unquote fake sequences from the generator from
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the ground truth data that was being fed to it.
The second g N set set up was specifically dedicated
to add detail and realism to the faces of the
target subjects. In some frames this appears to have worked
pretty well, and others there's a bit of an uncanny
valley thing or maybe even horror movie type element going on,
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similar to how some of the AI generated portraits that
I talked about in the previous episode introduced a bit
of unrealistic qualities to the various images. When shooting video
of the target subjects, the team captured images at one
hundred twenty frames per second to get enough data for
each subject. The sessions lasted for about twenty minutes. They
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used smartphone cameras to do it, since many smartphones allow
you to shoot video at this kind of frame rate
these days. They had their target subjects where close fitting
clothing that wasn't prone to wrinkling because the post recognition
tool they were using wasn't designed to encode information about clothing.
As for the source videos, the ones that would actually
create the motions that would be transferred to the targets,
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the team didn't have to worry about capturing images at
such a high frame rate. They could use videos of
just reasonable quality, meaning decent resolution and frame rate, and
their post detection tool would do its work and create
the stick figure that would serve as the guide for
the target motions later on. Because of that, the team
can really use any online video of sufficient quality to
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act as the source information for motion transfer. It doesn't
have to be a video shot specifically for that purpose.
In fact, one of the example videos the team used
in their demonstration was from a Bruno Mars music video
for That's what I Like. Before applying the motion transfer,
the team smoothed pose key points to reduce jitter in
the final output, and then the team applied the motion transfer.
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The stick figure motions were then transferred to the target
subjects and the result is pretty interesting. It is not seamless.
You can definitely tell something odd is going on, but
it is an indication of where things are going and
using adversarial networks could lead to more convincing motion transfers
in the future. Now, this could lead to all sorts
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of stuff nefarious and otherwise. You could imagine using it
to transform an average actor into a martial arts master,
or it might allow directors more freedom of casting, knowing
that if the actors they choose don't possess certain physical skills,
they can use this kind of technology to fake it,
but would also be used to fake footage to make
(31:02):
it looks like people like specific people are doing stuff
that they are not doing. It could be used to
spread misinformation and it likely will be, which means we'll
need to be on the lookout for signs of fakes,
which are going to get harder and harder to detect
as time goes on. And hey, you guys remember DARPA,
(31:22):
right because I just did a whole series of episodes
about them. Well, that agency has funded programs dedicated to
automating various forensic tools, including tools that could be used
to detect AI created forgeries in video and audio. Often
the secret is in the eyes. Most of these neural
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networks are trained on still images, so you send thousands
or tens of thousands of images if you have them,
of your various subjects, your target, and your source. But
most published still images don't show people with their eyes closed.
So I've moved my movements and blinking tends to be
a little wonky in these fake videos. You might watch
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one for a while and think, huh, that's weird. This
guy hasn't blinked for like ten minutes, or when they
blink it looks really strange. Well, that's an indication that
it's a fake video. There are other ones as well,
but DARPA is understandably keeping those quiet because not you know,
if if they publish how they figure out AI created
(32:27):
videos are in fact faked, then that gives the fakers
enough information to go back and improve their models. So
we're likely to see something akin to what happened with capture.
Specialists will develop new tools to detect a I generated media.
AI developers will then create more sophisticated models, and so
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it becomes kind of an arms race a seesaw, and
one benefit is that AI as a whole will improve,
but we may not be able to believe it when
we see it. Well, that wraps up this episode of fascinating,
somewhat disturbing topic, and uh, I'm sure we're gonna hear
a lot more about this in the years to come.
(33:10):
We've seen a lot of of sites banning deep fakes
outright because of the misinformation that they can spread. So
we're already seeing a reaction to this in various online communities,
so that's very interesting to me. But we're definitely gonna
keep seeing this continue. It's a it's a valid area
(33:32):
of AI research, so we will have to wait and
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(33:52):
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(34:14):
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(34:34):
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(34:57):
bousands of other topics because it has to have four.
Stock com
Get in touch with technology with tech Stuff from how
stuff works dot com. Hey there, and welcome to tech Stuff.
I'm your host, Jonathan Strickland. I'm an executive producer and
I love all things tech. And here's a fun fact
about getting older. As you age, stuff that you once
(00:25):
thought was impossible will become not only possible, but will
become the norm, and future generations won't even think about
what it must have been like before the impossible was commonplace.
Now this is true for every generation. It's not like
this is, you know, a brand new, groundbreaking observation. Plenty
(00:45):
of people have made it before me, but I want
to talk about a specific implementation. For example, with photography,
it used to be pretty difficult to manipulate pictures convincingly.
There have been photo and tools for decades, but generally
it took a great deal of skill and training, plus
(01:07):
access to specialized equipment to pull it off, especially in
the old film days. Now, gradually, tools like Photoshop made
it easier to manipulate digital images. Now it still requires
a certain level of skill to pull off a really
convincing job, and it's easy to make really badly manipulated images.
But as these tools became widely available, people began to
(01:30):
learn how to use them. We had to come to
the realization that we cannot necessarily believe our own eyes
when we're looking at a digital image. Now, the same
thing is happening with video footage. It's quite possible to
fake video footage, though again, if you want to do
it really well, it requires some skill, some specialized tools,
(01:52):
and to really get some expertise in it in order
to do it in a way that's really convincing. But
it's a pretty recent techn logical capability in fiction, however,
it's been around for a long time. I remember seeing
the movie Rising Sun back in the early nineteen nineties. Now,
in that film, Wesley Snipes plays a detective and Sean
(02:13):
Connery in his most convincing roles, since he played an
Egyptian immortal posing as a Spaniard with a Scottish accent
in Highlander, would play a Japanese customs and culture expert
Sean Connery. Anyway, the film is a mystery thriller, and
(02:34):
while the two are investigating a murder, they come into
possession of some video footage, and they find out that
video footage has actually been manipulated it was planted for
them to find so that would put them on the
wrong trail. One person's face was replaced with someone else's,
and in a somewhat comedic scene, the video editor who
(02:55):
is explaining this casually swaps the heads of Snipes and
Connor in real time in a video feed to show
off this capability, which might have been a tad unrealistic,
but today we are in a world in which video
manipulation of increasingly convincing quality is achievable in real time.
In fact, these days, it's possible to use sophisticated computer
(03:17):
algorithms to allow for manipulation of captured video, almost as
if the video was a computer generated cartoon reacting to
real time inputs like a video game controller, only instead
of it being a video game, it's a real person
on video. There are, of course, lots of ways this
technology could be used unethically, and one of the best
(03:40):
known has been the focus of this whole conversation around
video manipulation, and it comes from a former Reddit user
who went by the handle deep fakes. Now that handle
has become the shorthand for the general practice, which frequently,
but not exclusively, would involve replace the face of an
(04:01):
actor in a pornographic scene with someone else's face like
that of a celebrity, which is pretty darn unethical and creepy.
The name itself was a reference to the technology used
in the approach, so it relies on a process called
deep learning. Deep learning is a type of machine learning.
(04:22):
It's a sub type of machine learning that utilizes artificial
neural networks. And I've talked an awful lot about those
kind of networks recently, so I'm not going to go
over the whole thing again. Will give just a really
quick rundown to say, you have nodes, artificial neurons in
these networks that receive input from potentially multiple other nodes,
(04:45):
and then on that input, your artificial neuron that you're
looking at will perform some sort of weighted operation and
produce a single output. That single output can move on
to become one of many inputs for a different ARTIFICI
shoal neuron in that network, and so on and so forth.
Deep learning networks are very very large artificial neural networks,
(05:08):
and they can accept a large amount of training data.
This is a scalable approach. This means the larger the
network and the more data you can feed it, the
better it performs. This is different from any other machine
learning models. Those tend to hit a performance plateau once
you hit a certain size, which means that if you
were to add more nodes to the network, you wouldn't
(05:32):
necessarily see a comparative increase in performance. You would you
would kind of flatten out over time. In fourteen, a
deep learning expert named Andrew ng gave a talk at
Stanford about the best use cases for deep learning, and
he mentioned that it was particularly good at supervised learning tasks. Now,
(05:54):
these are the types of computer problems in which we
humans already know the answer, such as is there a
cat in this photograph? Humans can pick up on that
right away, assuming someone has not carefully hidden a cat
in a very busy image. But for a computer, this
is a much more difficult problem. Even if the picture
(06:14):
has a cat center stage, it can be tough for
a computer to figure that out using a supervised learning approach.
With a deep learning network, you can train a system
to recognize cats and images with a high degree of
success if you have a large amount of training data
to train the network to recognize cats. Now, in this
(06:36):
other case, that I was talking about. The Reddit user
called deep fakes started posting on Reddit in late twenties seventeen.
The user made an open source code version of a
deep learning algorithm and made it available for the purposes
of video manipulation and anyone could take advantage of it. Now. Specifically,
this algorithm was designed for face swapping. The algorithm would
(07:00):
allow you to put the face of one person onto
the body of another in video form, and it wasn't
always convincing. In fact, it could often be easily detectable
as fake if someone had not trained the model properly
before creating the video. But it did open up a
can of worms once the practice started getting media coverage. However,
(07:22):
the actual technology to pull this off was already a
couple of years old when deep fakes shared it. Back
in there was a group of researchers from Stanford then
and also the University of Erlanger Nuremberg and the Max
Planck Institute for Informatics who collectively published a paper that
was titled Face to Face Real Time Face Capture and
(07:45):
Reenactment of r GB Videos and that's a face, the
number two and face. The paper details the methodology the
group used to create a pretty incredible effect. The algorithm
could take the facial expressis from one person and transfer
them in real time to a video target. It was
(08:06):
like turning the video into a digital puppet. So you
might have a video loop of a celebrity running, and
preferably it's a loop that's easily repeatable without the repeat
being terribly noticeable, and that's your target video. So if
you just let it run, you just would see video
of someone sitting down, maybe looking around a little bit,
(08:27):
but that's it, nothing special. Then you would have a
source subject sitting in view of a consumer quality webcam,
no special equipment here, and that person could make different expressions,
including opening and closing their mouths, and the video target
would match them move for move, like a digital puppet. Moreover,
(08:49):
the source subject didn't have to wear any special gear.
They didn't have to have any special markers, none of
those dots that you would see with motion capture. None
of that was necessary. All the algorithm needed was a
video feed from a monocular camera, so you didn't even
need depth perception for this. There's a video of their
work on YouTube that shows off this process and includes
(09:10):
a loop of George W. Bush sitting for an interview.
The source subject can manipulate the face that Bush makes
just by making faces of his own, and the algorithm
would map those movements to the target video. And it's
pretty wild to see an image of moving image of
George W. Bush responding in real time to all of
(09:32):
these different facial expressions this guy is making. So how
did the team do this? It's one thing to say
a deep learning algorithm gave them this capability, but that's
not really an explanation. The paper definitively spells this out
in real technical detail. It starts off the explanation by saying, quote,
in our method, we first reconstruct the shape identity of
(09:56):
the target actor using a new global non rigid model
BA fast bundling approach based on a prerecorded training sequence.
As this pre process is performed globally on a set
of training frames, we can resolve geometric ambiguities common to
binocular reconstruction. At runtime. We tracked both the expressions of
the source and target actors video by a dense analysis
(10:17):
by synthesis approach based on a statistical facial prior end quote,
and it goes on in that vein throughout the paper
which means it gets pretty dense. But I think we
can suss out what's going on from a high level
if we just take a moment. But first, I'm going
to take a moment of my own to thank my sponsor.
(10:46):
So how did the Face to Face team build this tool? Well,
for each target video, they would collect a large sample
of footage and images and feed it to this deep
learning algorithm. This would be necess sarry to identify all
the points on the face that would move with various expressions,
as well as to capture images of the inside of
(11:08):
the target's mouth when he or she spoke. This is
because the video loop they used to create the manipulated
video would feature the target subject, typically with his or
her mouth closed, so it might be a section in
which the subject was sitting down for an interview and
listening to an interviewer's questions but not responding yet they
were just listening. The additional video would provide information about
(11:31):
the inside of the target subject's mouth, which could be
rendered in time on the target video performance when it
came time to do that. Their approach improved the scanning
technique to build face templates for both the source subject
who provides all the expressions and the target subject, who
mimics all the expressions. As the source subject makes different
(11:52):
facial expressions, the computer face template detects how the subjects
face changes or deforms over time. The computer model then
takes that information, saying, all right, well the lips moved
in this way, there was a grimace here or a
smile there, and transfer those motions to the targets face template,
(12:15):
which is matched to the target's actual face. This process
transfers the expressions over to the target, and so when
the source subject grimaces, the target grimaces, if the source
subject just it's still the target. Video will continue to
loop and the targets face won't change. The more video
footage you can get of your target and your source,
(12:38):
the better the computer algorithms are that create those face templates,
and the more natural the manipulation will appear on the
finished video. You also want a really good amount of
footage just to get all that extra information you need,
like the inside of the mouth, so that that can
all be extrapolated properly. You have to design a tool
that can encode an image called the training image, and
(13:00):
then decodes this data to reconstruct the image. So imagine
you've got a picture. The encoder essentially creates data based
on that image. It's like a description of that image.
The decoder takes the description and tries to rebuild the
image based on the description. I think of this like
(13:22):
that scene in Willy Wonka where Mike TV gets broken
up in a million little pieces and then gets reconstructed
on the television screen. So the second image is not
a copy. It's not like you made a copy of
the first one. It's like you built a new image
based on the first one. By the way, when you
start off with these uh these algorithms, those reconstructions tend
(13:45):
to look pretty bad. You have to continually train and
train and train and train the model so that it
gets better and better at producing a close representation of
the original image. When it does, it's reconstru struction. And
you would have both essentially decoders for both your source
(14:06):
subject and your target subject. Use the same encoder for both,
but two different decoders, one dedicated to your source, one
dedicated to your target. Then you would feed the reconstructed
images through the system again and again. This is called
back propagation. You do this over millions of times, typically
(14:27):
to improve this process, and then you're ready to really
switch switch things up. So let's say we've got two people.
We've got person one and we've got person two, and
you've been feeding images of both of these people through
the same encoder, but of course you have dedicated decoders
to produce the reconstruction. So person one has decode er
(14:47):
one and person two has decoder two. Now let's say
you're ready to put person two's face on person one's body. Well,
you would feed an image of person one into the encoder,
but you use the decoder for a person to to
reconstruct the image, and what you get is persons who's
(15:08):
face but mimicking the expression from person one. You, or
rather the computer algorithm, does this frame by frame on video,
and you end up with a video appearing to feature
one person when in fact it's just their face on
top of someone else, and it's their face making the
exact same expressions as whoever was originally in that video.
(15:32):
Now back over to deep fakes. Before long after the
Reddit user initially posted this code, folks over at Reddit,
we're taking this open source code and making more advanced
software based off of it. Soon there were desktop apps
that would take over all the hard parts of this process,
all the codey bits, if you will, of training a model.
(15:54):
Some of them would guide users into creating the data
that would be used to train the model and go
all the way through the process of creating the final
fake videos. Even with some of the more sophisticated versions,
there were tell tales signs of tampering. Typically some blurring
around images, particularly near chins and mouths. Those would be signs.
(16:14):
If there was any flicker, that was a sign if
you didn't take enough time to train the model. Typically
you would want to do several days of training at least.
If you didn't take that time, you might see some
really nasty blurring and flickering, and it would be a
dead giveaway that this was tampered. Video in writer, director,
(16:35):
and comedian Jordan's Peel demonstrated the power of this technology.
He showed how, with his impersonation of Barack Obama and
some manipulation software, he could create a fake public service
address when which the president would appear to say things
that he normally would never say. The technology behind this
(16:55):
made use of what is called a long short term
memory network or l s TM, to go into the
mechanics of that would require another podcast, but using an
approach similar to what I've already described, a team was
able to make a video of Obama apparently lip syncing
Peel's satirical message. The goal of this p s A
(17:16):
was beyond alert because fakes are getting harder to spot.
The University of Washington showed off this and They're Synthesizing
Obama project in which they took the audio from one
of President Obama's speeches and then used it to animate
his face in video from a different address that he
(17:36):
gave during his presidency. So in this example, the person
in the target video is the same person as the
source for the audio. But the point was pretty clear
that tech would soon make it possible to fake someone
saying or doing something. It just takes the right algorithms,
the right amount of training data, and the right amount
(17:58):
of time to get the model trained up enough to
do it smoothly. Now, this technology could be used to
do stuff that isn't related to malicious deception or for
pornography or anything along those lines. It could be used
in television and film for lots of stuff, including potentially
adding in actors who have passed away into a film.
(18:20):
Paired with similar work that's going on in voice synthesis,
you could end up with a convincing replacement, which means
we could make movies with dead actors taking on new
parts because we can synthesize their speech, we can synthesize
their appearance. You would still have someone else acting out
the part physically, but you would replace their image with
(18:42):
this actor's image. Or maybe you would want to use
this kind of technology just to make everyone think you
can cut a rug. This brings me to the University
of California, Berkeley and is the subject of a paper
titled Everybody Dance Now. The goal is a simple concept
that's actually really hard to pull off. What if you
(19:02):
were to take the movements of a professional dancer and
then map those movements onto the body of someone who
wasn't a dancer. What if you could create a video
in which literally anyone would appear to move like a skilled,
trained dancer. And how the heck would that be possible. Well,
at the heart of the team's efforts was something I
(19:23):
talked about in a recent episode of tech Stuff about
an AI generated portrait, and that would be generative adversarial
networks or g A n s. These use a pair
of artificial neural networks in competition against each other. So
since I covered this recently, i'll just give again a
super quick high level summary. You've got one network that
(19:46):
has a specific job, such as trying to create an
original image of a cat. We'll go back to the
cat pictures. That's one of my favorite ones because it
was one of the early use cases of neural networks
that I remember encountering when I was doing research. Now,
let's say you've got your second network. Your second network
has the specific job of evaluating pictures of cats to
(20:08):
determine if they are valid, meaning is this a real
picture of a cat that's part of the training material
that I'm accepting, or is this, in fact a fake
that was created by a computer program the other neural network.
So you've got one network trying to fool the other network.
And these networks get better at what they do over time,
(20:31):
they improve, So your counterfeit network is getting better and
better at making fake pictures of cats, and your detector
network is getting better and better at detecting fake images
of cats. Now, typically this requires humans to give feedback
or tweaking weight values along the networks, but they do
(20:52):
get better over time. So if the network trying to
create a picture of a cat gets the feedback of sorry, buddy,
but they're onto you, then it can try again and
adjust it's approach slightly in an effort to fool the
second network. If the second network gets the feedback you'll
let this one slip by and it's fake, then it
will adjust or it will be adjusted to look out
(21:13):
for any tailtale signs that it had missed in that
earlier evaluation. Over time, the two networks working against each
other will create the ultimate result of better and better
computer generated content, whether it's an image of a cat
or a sonnet, or a song or a video. Now
(21:34):
that doesn't mean that these computer generated things are at
the same level as human generated stuff, especially when it
comes to text. I've seen a lot of song lyrics
that were inscrutable even by my old man standards. So
I think that we're a long way away from getting
to a point where they can fool us in every case.
(21:57):
But with video they're getting pretty darn good. Now, this
team had two groups of subjects, and so you had
your source subjects and your target subjects. The source in
this case, were the people who could dance, so like
ballet dancers, hip hop dancers and that sort of stuff.
They legit know how to move. They would demonstrate various
(22:19):
dances on video. The second group of subjects were your
target subjects. These were not trained dancers. They were to
go through a series of moves and poses, essentially aping
as best they could the movements of trained dancers, and
the goal of this pair of networks was to smooth
(22:40):
the movements out and adjust the timing so that these
untrained dancers would appear to move more like their groovy
source subject counterparts. I'll explain more in just a moment,
but first let's take another quick break to thank our sponsor.
(23:01):
According to the Everybody Dance Now paper, the team would
transfer motion between the sources to the target through an
end to end pixel based pipeline. So here's how that's done.
Because if you're like me, that phrase meant next to
nothing to you. So specifically, the group used three stages
(23:22):
to take the movements of one person and transpose them
to a target person. Those three were pose detection, global
pose normalization, and mapping from normalized pose stick figures to
the target subject. Post detection involves teaching machines, in other words,
computers how to interpret images to determine where key body
(23:45):
points are, like elbows, knees, hips, shoulders, the head, that
kind of stuff. That first requires that you teach the
machine to recognize those points in the first place. So
first you have to train a machine to recogniz eyes
those points and identify them with a target level of accuracy.
It's pretty typical to represent these joints as as points
(24:08):
in a stick figure, so each point represents another joint
or point of articulation. The lines represent the trunk of
the body, the limbs, the head. You end up with
a stick figure. If your machine learning mechanism was a
good one, the machine should be able to overlay a
stick figure on top of any image of a person posing,
and the stick figure should more or less conform to
(24:30):
that image, including where the actual joints are. So if
you have someone standing there in the classic Peter Pan
pose of their their fists on their hips uh and
their their arms out of kimbo, then it should draw
a stick figure that's essentially aping the same thing and
be able to overlay it on top of the original image.
Now these days this can be done in real time. So,
(24:52):
for example, there's a team at Google Creative Lab that
used a machine learning model of pose net and created
a JavaScript version with TensorFlow, which is an open source
software library often used for machine learning. And with this
tool you can do real time pose estimation through a
browser and a webcam. The application doesn't have any technology
(25:12):
related to identifying the person in the image. It's just
quote unquote interested in what the person is doing, not
who the person is. So you can actually run this
on your own machine in a browser, and you can
pose in front of a webcam and you'll see the
little stick figure uh painted on top of your image
on the computer. Essentially, so every time you move, every
time you bend a joint, you will see the stick
(25:34):
figure doing the same thing, um mapped on top of you.
The Berkeley team made use of a pre trained pose detector,
meaning they didn't build a new one, which helps save
a lot of time and expense on their project. Now
people come in all shapes and sizes. In the video
the team released, they showed off subjects who included a
(25:54):
woman who appeared to be of around average height and
a man who appeared to be pretty darn Tallman transfer
method that would only work between a subject and a
target who are of similar shape and size would be
pretty limited. So the purpose of the global pose normalization
stage is to account for all the differences between the
(26:14):
source and the target subjects and the locations within the
frame of the camera. Without this step, the motion transfer
might appear ghoulish. We don't have all the same proportions, right,
so a mismatch might mean a target's limbs would appear
to bend in places that were clearly not natural joints.
(26:35):
All you need to do is see an arm bend
where an arm isn't supposed to bend, and that's going
to ski the out quite a bit. Makes an effective
horror movie experience, but not one that would produce convincing
motion transfer. Now, there are a lot of ways that
the team could have gone about normalizing the poses, but
their choice seems particularly clever to me. They measured the
heights and ankle positions of the various subjects and used
(26:58):
linear mapping between the closest and farthest ankle positions in
both videos to normalize the stick figure for the target subjects.
The program would calculate the scale of the figure as
well as the scale of motion from frame to frame.
And I think that's pretty darn cool because it wasn't
just accounting for the size of the subjects to get
(27:19):
all the joints right, but also to make sure the
scale of the movements with respect to the body size
and proportions would remain the same. So a tall person
with really long limbs moving their arms in really big, big,
bold gestures, if you tried to transfer that motion to
someone who was of smaller stature, it could really look disturbing.
(27:43):
But by using this scaling approach, the movements on the
smaller person would be proportionate in size to the movements
of the larger person. The team would use two of
the Generative Adversarial Network setups to work on making a
convincing final video. The first was dedicated to image to
(28:04):
image translation, attempting to manipulate the image of the target
subjects that would follow the motions made from the pose
detection process, and like all g a N setups, this
included the generator, which would attempt to create a convincing
sequence of images, and the discriminators, which tried to weed
out the quote unquote fake sequences from the generator from
(28:25):
the ground truth data that was being fed to it.
The second g N set set up was specifically dedicated
to add detail and realism to the faces of the
target subjects. In some frames this appears to have worked
pretty well, and others there's a bit of an uncanny
valley thing or maybe even horror movie type element going on,
(28:46):
similar to how some of the AI generated portraits that
I talked about in the previous episode introduced a bit
of unrealistic qualities to the various images. When shooting video
of the target subjects, the team captured images at one
hundred twenty frames per second to get enough data for
each subject. The sessions lasted for about twenty minutes. They
(29:07):
used smartphone cameras to do it, since many smartphones allow
you to shoot video at this kind of frame rate
these days. They had their target subjects where close fitting
clothing that wasn't prone to wrinkling because the post recognition
tool they were using wasn't designed to encode information about clothing.
As for the source videos, the ones that would actually
create the motions that would be transferred to the targets,
(29:30):
the team didn't have to worry about capturing images at
such a high frame rate. They could use videos of
just reasonable quality, meaning decent resolution and frame rate, and
their post detection tool would do its work and create
the stick figure that would serve as the guide for
the target motions later on. Because of that, the team
can really use any online video of sufficient quality to
(29:52):
act as the source information for motion transfer. It doesn't
have to be a video shot specifically for that purpose.
In fact, one of the example videos the team used
in their demonstration was from a Bruno Mars music video
for That's what I Like. Before applying the motion transfer,
the team smoothed pose key points to reduce jitter in
the final output, and then the team applied the motion transfer.
(30:17):
The stick figure motions were then transferred to the target
subjects and the result is pretty interesting. It is not seamless.
You can definitely tell something odd is going on, but
it is an indication of where things are going and
using adversarial networks could lead to more convincing motion transfers
in the future. Now, this could lead to all sorts
(30:40):
of stuff nefarious and otherwise. You could imagine using it
to transform an average actor into a martial arts master,
or it might allow directors more freedom of casting, knowing
that if the actors they choose don't possess certain physical skills,
they can use this kind of technology to fake it,
but would also be used to fake footage to make
(31:02):
it looks like people like specific people are doing stuff
that they are not doing. It could be used to
spread misinformation and it likely will be, which means we'll
need to be on the lookout for signs of fakes,
which are going to get harder and harder to detect
as time goes on. And hey, you guys remember DARPA,
(31:22):
right because I just did a whole series of episodes
about them. Well, that agency has funded programs dedicated to
automating various forensic tools, including tools that could be used
to detect AI created forgeries in video and audio. Often
the secret is in the eyes. Most of these neural
(31:43):
networks are trained on still images, so you send thousands
or tens of thousands of images if you have them,
of your various subjects, your target, and your source. But
most published still images don't show people with their eyes closed.
So I've moved my movements and blinking tends to be
a little wonky in these fake videos. You might watch
(32:05):
one for a while and think, huh, that's weird. This
guy hasn't blinked for like ten minutes, or when they
blink it looks really strange. Well, that's an indication that
it's a fake video. There are other ones as well,
but DARPA is understandably keeping those quiet because not you know,
if if they publish how they figure out AI created
(32:27):
videos are in fact faked, then that gives the fakers
enough information to go back and improve their models. So
we're likely to see something akin to what happened with capture.
Specialists will develop new tools to detect a I generated media.
AI developers will then create more sophisticated models, and so
(32:49):
it becomes kind of an arms race a seesaw, and
one benefit is that AI as a whole will improve,
but we may not be able to believe it when
we see it. Well, that wraps up this episode of fascinating,
somewhat disturbing topic, and uh, I'm sure we're gonna hear
a lot more about this in the years to come.
(33:10):
We've seen a lot of of sites banning deep fakes
outright because of the misinformation that they can spread. So
we're already seeing a reaction to this in various online communities,
so that's very interesting to me. But we're definitely gonna
keep seeing this continue. It's a it's a valid area
(33:32):
of AI research, so we will have to wait and
see how it all plays out. If you guys have
any suggestions for future episodes of tech Stuff, why not
send me a message. You can go over to our
website that is Text Stuff podcast dot com. You'll find
all the different ways to contact me. I look forward
to hearing from you. Make sure you check out our
(33:52):
store over at t public dot com slash tech Stuff
by some merchandise. You can make sure that you get
all the really cool T shirts like prove to Me
You're not a Robot. That one's pretty appropriate for this
particular episode. And remember every single purchase you make goes
to help the show, so we greatly appreciate it. Also,
if you haven't heard, we have been nominated for an
(34:14):
I Heart Radio Podcast Award. It's the first year I
Heart Radio is giving out podcast awards. We are nominated
in the Science and Technology category. You can go online
and visit the I Heart Radio Podcast Awards page and
vote up to five times a day for your favorite podcasts.
If you wanted to. You could dedicate all five of
(34:34):
those votes every single day to us. I would not
complain if you did that. It would be really cool
to win that award, but make sure you check it out.
There may be lots of shows there that you truly
love and you want to throw your support behind them.
That would be really cool with you, And I'll talk
to you again really soon for more on this and
(34:57):
bousands of other topics because it has to have four.
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TechStuff News
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Oz Woloshyn
Karah Preiss
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