Speaker 1: Welcome to tech Stuff, a production from I Heart Radio.
Speaker 1: Hey there, and welcome to tech Stuff. I'm your host,
Speaker 1: Jonathan Strickland. I'm an executive producer with I Heart Radio,
Speaker 1: and how the tech are you. I am currently on
Speaker 1: vacation celebrating my anniversary, and I didn't want to leave
Speaker 1: you without an episode, So the episode We're going to
Speaker 1: play for You was recorded and published on September seven,
Speaker 1: twenty twenty. It is called deep learning and deep fakes,
Speaker 1: and recent developments in the deep fakes field include researchers
Speaker 1: creating tools that can detect tells in artificial voices, for example.
Speaker 1: But really, when you think about that, it's just a
Speaker 1: see saw like pattern that will see deep fake technology
Speaker 1: improve over time, and then our ability to detect deep
Speaker 1: fakes will improve, and this will keep going until one
Speaker 1: side or the other has the edge permanently. Now we
Speaker 1: kind of talk about that in this episode. In fact,
Speaker 1: also deep fakes are very much in the spotlight literally
Speaker 1: on the popular TV series America's Got Talent, A team
Speaker 1: from the startup Metaphysics made it all the way to
Speaker 1: the final round of the competition by creating deep fake
Speaker 1: copies of the famous judges on the show all in
Speaker 1: real time. It's equal parts entertaining and terrifying. Okay, maybe
Speaker 1: entertaining terrifying. Anyway, enjoy this episode deep learning and deep Fakes. Now,
Speaker 1: before I get into today's episode, I want to give
Speaker 1: a little listener warning here. The topic at hand involves
Speaker 1: some adult content, including the use of technology to do
Speaker 1: stuff that can be unethical, illegal, hurtful, and just plain awful. Now,
Speaker 1: I think this is an important topic, but I wanted
Speaker 1: to give a bit of a heads up at this
Speaker 1: are of the episode, just in case any of you
Speaker 1: guys are listening to a podcast on like a family
Speaker 1: road trip or something. I think this is an important
Speaker 1: topic and I think everyone should know about it and
Speaker 1: think about it. But I also respect that for some
Speaker 1: people this subject might get a bit taboo. So let's
Speaker 1: go on with the episode. Back in ninete a movie
Speaker 1: called Rising Sun, directed by Philip Kaufman, based on a
Speaker 1: Michael Crichton novel and starring Wesley Snipes and Sean Connery
Speaker 1: came out in theaters. Now, I didn't see it in theaters,
Speaker 1: but I did catch it when it came on you know,
Speaker 1: HBO or Cinemax or something. Later on the movie included
Speaker 1: a sequence that I found to be totally unbelievable. And
Speaker 1: I'm not talking about buying into Sean Connery being an
Speaker 1: expert on Japanese culture and business practices. Actually, side note,
Speaker 1: Sean Connery has an interesting history of playing unlikely characters,
Speaker 1: such as in high Lander, where he played an immortal
Speaker 1: who is supposedly Egyptian, then who lived in feudal Japan
Speaker 1: and ended up in Spain where he became known as Ramirez,
Speaker 1: and all the while he's talking to a Scottish Highlander
Speaker 1: who's played by a Belgian actor. But I'm getting way
Speaker 1: off track here. Besides, I've heard Crichton actually wrote the
Speaker 1: character while thinking of Connery, So you know, what the
Speaker 1: heck do I know? In the film, Snips and Connery
Speaker 1: are investigators, and they're looking into a homicide that happened
Speaker 1: at a Japanese business but on American soil. The security
Speaker 1: system in the building captured video of the homicide and
Speaker 1: the identity of the killer appears to be a pretty
Speaker 1: open and shut case. But that's not how it all
Speaker 1: turns out. The investigators talked to a security expert played
Speaker 1: by Tia Carrera, and she demonstrates in real time how
Speaker 1: video footage can be altered. She records a short video
Speaker 1: of Connery and snipes loads that onto a computer. Her
Speaker 1: freezes a frame of the video and essentially performs a
Speaker 1: cut and paste job swapping the heads of our two
Speaker 1: lead characters. Then she resumes the video and the head
Speaker 1: swap remains in place, and that head swap stuff is possible.
Speaker 1: I mean, clearly it has to be possible, because you
Speaker 1: actually do see that effect in the film itself. But
Speaker 1: it takes a bit more than a quick cut and
Speaker 1: paste job. But we'll leave off of that for now.
Speaker 1: The whole point of that sequence, apart from showing off
Speaker 1: some cinema magic, is to demonstrate to the investigators that video,
Speaker 1: like photographs, can be altered. The expert has detected a
Speaker 1: blue halo around the face of the supposed murderer in
Speaker 1: the footage, indicating that some sort of trickery has happened.
Speaker 1: She also reveals that she cannot magically restore the video
Speaker 1: to its previous unaltered state, which I think was actually
Speaker 1: a nice change of pace for a movie. By the way,
Speaker 1: I think this movie is really, you know, not good,
Speaker 1: like not worth your time, but that's my opinion anyway.
Speaker 1: For years, this kind of video sorcery was pretty much
Speaker 1: limited to the film and TV industries. It usually required
Speaker 1: a lot of pre planning beforehand, so it wasn't as
Speaker 1: simple as just taking footage that was already shot and
Speaker 1: changing it in post on a whim with a couple
Speaker 1: of clicks of a button. If it were, we would
Speaker 1: see a lot fewer mistakes left in movies and television
Speaker 1: because you could catch it later and just fix it.
Speaker 1: But the tricks were possible, they were just difficult to
Speaker 1: pull off. It just wasn't something you or I would
Speaker 1: ever encounter in our day to day lives. But today
Speaker 1: we live in a different world, a world that has
Speaker 1: examples of synthetic media. Commonly referred to as deep fakes.
Speaker 1: These are videos that have been altered or generated so
Speaker 1: that the subject of the video is doing something that
Speaker 1: they probably really would or could never do. They've brought
Speaker 1: into question whether or not video evidence is even reliable,
Speaker 1: much as the film Rising Sun was talking about. We
Speaker 1: already know that eyewitness testimony is terribly unreliable. Our perception
Speaker 1: and memories play tricks on us, and we can quote
Speaker 1: unquote remember stuff that just didn't happen the way things
Speaker 1: actually unfolded in reality. But now we're looking at video
Speaker 1: evidence and potentially the same light. I mean, it's scary.
Speaker 1: So today we're going to learn about synthetic media, how
Speaker 1: it can be generated, the implications that follow with that
Speaker 1: sort of reality, and ways that people are trying to
Speaker 1: counteract a potentially dangerous threat. You know, fun stuff. Now, first,
Speaker 1: the term synthetic media has a particular meaning. It refers
Speaker 1: to art created through some sort of automated process, so
Speaker 1: it's a largely hands off approach to creating the final
Speaker 1: art piece. Now, under that definition, the example of rising
Speaker 1: Sun would not apply here because we see in the
Speaker 1: film and presumably this happens in the book as well,
Speaker 1: but I haven't read the book that a human being
Speaker 1: actually changes that. People have used tools to alter the
Speaker 1: video footage. This would be more like using photoshop to
Speaker 1: touch up a still image, with the computer system presumably
Speaker 1: doing some of the work in the background to keep
Speaker 1: things matched up. Either that or you would need to
Speaker 1: alter each image in the footage frame by frame, or
Speaker 1: use some sort of matt approach. To learn more about Matts,
Speaker 1: you can listen to my episode about how blue and
Speaker 1: green screens work. Synthetic media as a general practice has
Speaker 1: been around for centuries. Artists have set up various contraptions
Speaker 1: to create works with little or no human guidance. In
Speaker 1: the twentieth century we started to see a movement called
Speaker 1: generative art take form. This type of art is all
Speaker 1: about creating a system that then creates or generates the
Speaker 1: finished art piece. That would mean that the finished work,
Speaker 1: such as a painting, wouldn't reflect the feelings or thoughts
Speaker 1: of the artists who created the system. In fact, it
Speaker 1: starts to raise the question what is the art? Is
Speaker 1: it the painting that came about due to a machine
Speaker 1: following a program of some sort, or is the art
Speaker 1: the program itself? Is the art the process by which
Speaker 1: the painting was made? Now, I'm not here to answer
Speaker 1: that question. I just think it is an interesting question
Speaker 1: to ask. Sometimes people ask much less polite questions, such
Speaker 1: as is it art at all? Some art critics went
Speaker 1: out of their way to dismiss generative art in the
Speaker 1: early days. They found it insulting, but hey, that's kind
Speaker 1: of the history of art and general Each new movement
Speaker 1: in art inevitably finds both supporters and critics as it emerges.
Speaker 1: If anything, you might argue that such a response legitimizes
Speaker 1: the movement in you know, a weird way. If people
Speaker 1: hate it, it must be something. In two thousand eighteen,
Speaker 1: an artist collective called Obvious located out of Paris, France.
Speaker 1: They submitted portrait style paintings that were created not by
Speaker 1: an actual human painter, but by an artificially intelligent system.
Speaker 1: Now they looked a lot like typical eighteenth century style portraits.
Speaker 1: There was no attempt to pass off the portrait as
Speaker 1: if it were actually made by a human artist. In fact,
Speaker 1: the appeal of the piece was largely due to it
Speaker 1: being synthetically generated. It went to auction at Christie's and
Speaker 1: the AI created painting fetched more than four hundred thousand dollars.
Speaker 1: And the way the group trained their AI is relevant
Speaker 1: to our discussion about deep fakes. The collective relied on
Speaker 1: a type of machine learning called generative adversarial networks or
Speaker 1: g a N, which in turn is depending on deep learning.
Speaker 1: So it looks like we've got a few things we're
Speaker 1: gonna have to define here. Now, I'm going to keep
Speaker 1: things fairly high level, because, as it turns out, there
Speaker 1: are a few different ways to create machine learning models,
Speaker 1: and to go through all of them in exhaustive detail
Speaker 1: would represent a university level course in machine learning. I
Speaker 1: have neither the time for that nor the expertise. I
Speaker 1: would do a terrible job, so we'll go with a
Speaker 1: high level perspective here. First. A generative adversarial network uses
Speaker 1: two systems. You have a generator and you have a discriminator.
Speaker 1: Both of these systems are a type of neural network.
Speaker 1: A neural network is a computing model that is inspired
Speaker 1: by the way our brains work. Our brains contain billions
Speaker 1: of neurons, and these neurons work together, communicating through electrical
Speaker 1: and chemical signals, controlling and coordinating pretty much everything in
Speaker 1: our bodies. With computers, the neurons are nodes. The job
Speaker 1: of a node is, you know, supposed to be kind
Speaker 1: of like a neuron cell in the brain. It's to
Speaker 1: take in multiple weighted input values and then generate a
Speaker 1: single output value. Now, the word weighted w E I
Speaker 1: G H T E D weighted is really important here
Speaker 1: because the larger and inputs weight the more that input
Speaker 1: will have an effect on whatever the output is. So
Speaker 1: it kind of comes down to which inputs are the
Speaker 1: most important for that nodes particular function. Now, if I
Speaker 1: were to make an analogy, I would say, your boss
Speaker 1: hands you three tasks to do. One of those tasks
Speaker 1: has the label extremely important, and the second task has
Speaker 1: the label critically important, and the third task has a
Speaker 1: label saying you should have finished that one before it
Speaker 1: was handed to you. Okay, so that's just some sort
Speaker 1: of snarky office humor that I need to get off
Speaker 1: my chest. But more seriously, imagine a node accepting three inputs.
Speaker 1: In this example, input one has a fift weight, input
Speaker 1: two has a weight, and input three has a ten
Speaker 1: percent weight That adds up to and that would tell
Speaker 1: you that the output that node generates will be most
Speaker 1: affected by input one, followed by input two, and then
Speaker 1: input three would have a smaller effect on whatever the
Speaker 1: output is. Each node applies a nonlinear transformation on the
Speaker 1: input values, again affected by each inputs weight value, and
Speaker 1: that generates the output value. The details of that really
Speaker 1: are not important are our episode. It involves performing changes
Speaker 1: on variables that in turn change the correlation between variables,
Speaker 1: and it gets a bit Matthew, and we would get
Speaker 1: lost in the weeds. Pretty quickly. The important thing to
Speaker 1: remember is that a node within a neural network takes
Speaker 1: in a weighted sum of inputs, then performs a process
Speaker 1: on those inputs before passing the result on as an output.
Speaker 1: Then some other node a layer down will accept that output,
Speaker 1: along with outputs from a couple of other nodes one
Speaker 1: layer up, and then we'll perform an operation based on
Speaker 1: those weighted inputs and pass that on to the next layer,
Speaker 1: and so on. So these nodes are in layers, like
Speaker 1: you know a cake. One layer of notes processes some inputs,
Speaker 1: they send it onto the next layer of nodes, and
Speaker 1: then that one does onto the next one, and the
Speaker 1: next one and so on. This isn't a new idea.
Speaker 1: Computer scientists began theorizing and experimenting with neural network approaches
Speaker 1: as far back as the nineteen fifties with the perceptron,
Speaker 1: which was a hypothetical system that was described by Frank
Speaker 1: Rosenblatt of Cornell University. But it wasn't until the last
Speaker 1: decade that computing power and our ability to handle a
Speaker 1: lot of data reached a point where these sort of
Speaker 1: learning models could really take off. The goal of this
Speaker 1: system is to train it to perform a particular task
Speaker 1: within a certain level of precision. The weights I mentioned
Speaker 1: are adjustable, so you can think of it as teaching
Speaker 1: a system which bits are the most important in order
Speaker 1: to do whatever it is the system is supposed to
Speaker 1: do in order to achieve your task. These are the
Speaker 1: bits that are the most important and therefore should matter
Speaker 1: the most when you weigh a decision. This is a
Speaker 1: bit easier if we talk about a similar system with
Speaker 1: the version of IBM S Watson that played on Jeopardy.
Speaker 1: That system famously was not connected to the Internet. It
Speaker 1: had to rely on all the information that was stored
Speaker 1: within itself. When the system encountered a clue in Jeopardy,
Speaker 1: it would analyze the clue, and then it would reference
Speaker 1: its database to look for possible answers to whatever that
Speaker 1: clue was. The system would weigh those possible answers and
Speaker 1: attempt to determine which, if any, were the most likely
Speaker 1: to be correct. If the certainty was over a certain threshold,
Speaker 1: such as sure, the system would buzz in with its answer.
Speaker 1: If no response rose above that threshold, the system would
Speaker 1: not buzz in. So you could say that Watson was
Speaker 1: playing the game with a best guess sort of approach.
Speaker 1: Neural networks do essentially that sort of processing. With this
Speaker 1: particular type of approach, we know what we want the
Speaker 1: outcome to be, so we can judge whether or not
Speaker 1: the system was successful. After each attempt, we can adjust
Speaker 1: weight on the input between nodes to refine the decision
Speaker 1: making process to get more accurate results. If the system
Speaker 1: succeeds in its task, we can increase the weights that
Speaker 1: contributed to the system picking the correct answer and thus
Speaker 1: decrease the inputs that did not contribute to the successful response.
Speaker 1: If the system done messed up and gave the wrong answer,
Speaker 1: then we do the opposite. We look at the inputs
Speaker 1: that contributed to the wrong answer, we diminish their weights,
Speaker 1: and we increase the weights of the other input and
Speaker 1: then we run the test again a lot. I'll explain
Speaker 1: a bit more about this process when we come back,
Speaker 1: but first let's take a quick break. Early in the
Speaker 1: history of neural networks, computer scientists were hitting some pretty
Speaker 1: hard stops do to the limitations of computing power at
Speaker 1: the time. Early networks were only a couple of layers deep,
Speaker 1: which really meant they weren't terribly powerful, and they could
Speaker 1: only tackle rudimentary tasks like figuring out whether or not
Speaker 1: a square is drawn on a piece of paper that
Speaker 1: isn't terribly sophisticated. In six David Rummelhart, Jeffrey Hinton, and
Speaker 1: Ronald Williams published a lecture titled learning representations by back
Speaker 1: propagating errors. This was a big breakthrough with deep learning.
Speaker 1: This all has to do with a deep learning system
Speaker 1: improving its ability to complete a specific task. And basically
Speaker 1: the algorithm's job is to go from the output layer,
Speaker 1: you know, where the system has made a decision, and
Speaker 1: then work backward through the neural network, adjusting the weights
Speaker 1: that led to an incorrect decision. So let's say it's
Speaker 1: a system that is looking to figure out whether or
Speaker 1: not a hat is in a photograph and it says,
Speaker 1: there's a cat in this picture, and you look at
Speaker 1: the picture and there is no cat there. Then you
Speaker 1: would look at the inputs one level back just before
Speaker 1: the system said here's a picture of a cat, and
Speaker 1: you'd say, all right, which of these inputs lad the
Speaker 1: system to believe this was a picture of a cat?
Speaker 1: And then you would adjust those Then you would go
Speaker 1: back one layer up, So you're working your way up
Speaker 1: the model and say which inputs here led to it
Speaker 1: giving the outputs that led to the mistake, and you
Speaker 1: do this all the way up until you get up
Speaker 1: to the input level at the top of the computer model.
Speaker 1: You are back propagating, and then you run the test
Speaker 1: again to see if you've got improvement. It's exhaustive, but
Speaker 1: it's also drastically improved neural network performance, much faster than
Speaker 1: just throwing more brute force to it. The algorithm is
Speaker 1: entually is checking to see if a small change in
Speaker 1: each input value received by a layer of nodes would
Speaker 1: have led to a more accurate results. So it's all
Speaker 1: about going from that output working your way backward. In
Speaker 1: two thousand twelve, Alex Krajewski published a paper that gave
Speaker 1: us the next big breakthrough. He argued that a really
Speaker 1: deep neural network with a lot of layers could give
Speaker 1: really great results if you paired it with enough data
Speaker 1: to train the system. So you needed to throw lots
Speaker 1: of data at these models, and it needed to be
Speaker 1: an enormous amount of data. However, once trained, the system
Speaker 1: would produce lower error rates. So yeah, I would take
Speaker 1: a long time, but you would get better results. Now,
Speaker 1: at the time, a good error rate for such a
Speaker 1: system was that means one out of four conclusions the
Speaker 1: system would come to would be wrong. If you ran
Speaker 1: it across a long enough number of decisions, you would
Speaker 1: find that one out of every four wasn't right. The
Speaker 1: system that Alex's team worked on produced results that had
Speaker 1: an error rate of six percent, so much lower. And
Speaker 1: then in just five years, with more improvements to this process,
Speaker 1: the classification error rate had dropped down to two point
Speaker 1: three percent for deep learning systems. So from to two
Speaker 1: point three percent, it was really powerful stuff. Okay, so
Speaker 1: you've got your artificial neural network. You've got your layers
Speaker 1: and layers of nodes. You've adjusted the weights of the
Speaker 1: inputs into each node to see if your system can identify,
Speaker 1: you know, pictures of cats, and you start feeding images
Speaker 1: to this system, lots of them. This is the domain
Speaker 1: that you are feeding to your system. The more images
Speaker 1: you can feed to it, the better. And you want
Speaker 1: a wide variety of images of all sorts of stuff,
Speaker 1: not just of different types of cats, but stuff that
Speaker 1: most certainly isn't not a cat, like dogs, or cars
Speaker 1: or chartered public accountants. You name it, and you look
Speaker 1: to see which images the system identifies correctly and which
Speaker 1: ones it screws up, both which images have cats in
Speaker 1: it that actually don't have cats in it, or images
Speaker 1: the system has identified as saying there is no cat here,
Speaker 1: but there is a cat there. This guides you into
Speaker 1: adjusting the weights again and again, and you start over
Speaker 1: and you do it again, and that's your basic deep
Speaker 1: learning system, and it gets better over time as you
Speaker 1: train it. It learns. Now, let's transition over to the
Speaker 1: adversarial systems I mentioned earlier, because they take this and
Speaker 1: twist it a little bit. So you've got to artificial
Speaker 1: neural networks and they are using this general approach to
Speaker 1: deep learning, and you're setting them up so that they
Speaker 1: feed into each other. One network. The generator has the
Speaker 1: task to learn how to do something such as create
Speaker 1: an eighteenth century style portrait based off lots and lots
Speaker 1: of examples of the real thing. The domain the problem
Speaker 1: domain the second network. The discriminator has a different job.
Speaker 1: It has to tell the difference between authentic portraits that
Speaker 1: came from the problem domain and computer generated portraits that
Speaker 1: came from the generator itself. So essentially, the discriminator is
Speaker 1: like the model I mentioned earlier that was identifying pictures
Speaker 1: of cats, It's doing the same sort of thing, except
Speaker 1: instead of saying cat or no cat, it's saying real
Speaker 1: portrait or computer generated portrait. So there are essentially two
Speaker 1: outcomes the discriminator could reach, and that's whether or not
Speaker 1: an images computer generated or it wasn't. So do you
Speaker 1: see where this is going? You train up both models.
Speaker 1: You have the generator attempt to make its own version
Speaker 1: of something such as that eighteenth century portrait. It does
Speaker 1: so it designs the portrait it based on what the
Speaker 1: model believes are the key elements of a portrait, so
Speaker 1: things like colors, shapes, the ratio of size, like you know,
Speaker 1: how large should the head be in relation to the body.
Speaker 1: All of these factors and many more come into play.
Speaker 1: The generator creates its own idea of what a portrait
Speaker 1: is supposed to look like, and chances are the early
Speaker 1: rounds of this will not be terribly convincing. The results
Speaker 1: are then fed to the discriminator, which tries to suss
Speaker 1: out which of the images fed to it are computer
Speaker 1: generated and which ones aren't. After that round, both models
Speaker 1: are tweaked. The generator adjusts input weights to get closer
Speaker 1: to the genuine article, and the discriminator adjust weights to
Speaker 1: reduce false positives or to catch computer generated images. And
Speaker 1: then you go again and again and again and again,
Speaker 1: and they both get better over time. So, assuming everything
Speaker 1: is working properly, over time, the adjustment of input weights
Speaker 1: will lead to more convincing results, and given enough time
Speaker 1: and enough repetition, you'll end up with a computer generated
Speaker 1: painting that you can auction off for nearly half a
Speaker 1: million dollars. Though keep in mind that huge price relates
Speaker 1: back to the novelty of it being an early AI
Speaker 1: generated painting. It would be shocking to me if we
Speaker 1: saw that actually become a trend. Also, the painting, while interesting,
Speaker 1: isn't exactly so astounding as to make you think there's
Speaker 1: no way a machine did that. You'd look at them
Speaker 1: and go, yeah, I can imagine a machine did that. One.
Speaker 1: A group of computer scientists first described the general adversarial
Speaker 1: network architecture in a paper in two thousand and fourteen,
Speaker 1: and like other neural networks, these models require a lot
Speaker 1: of data. The more the better. In fact, smaller data
Speaker 1: sets means the models have to make some pretty big assumptions,
Speaker 1: and you tend to get pretty lousy results. More data,
Speaker 1: as in more examples, teaches the models more about the
Speaker 1: parameters of the domain, whatever it is they are trying
Speaker 1: to generate. It refines the approach. So if you have
Speaker 1: a sophisticated enough pair of models and you have enough
Speaker 1: data to fill up a domain, you can generate some
Speaker 1: convincing material, and that includes video. And this brings us
Speaker 1: around to deep fakes. And in addition to generative adversarial networks,
Speaker 1: a couple of other things really converged to create the
Speaker 1: techniques and trends and technology that would allow for deep
Speaker 1: fakes proper. In Malcolm Slaney, Michelle Covell, and Christoph Bregler
Speaker 1: wrote some software that they called the Video Rewrite Program.
Speaker 1: The software would analyze faces and then create or synthesize
Speaker 1: lip animation which could be matched to pre recorded audio.
Speaker 1: So you could take some film footage of a person
Speaker 1: and and reanimate their lips so that they could appear
Speaker 1: to say all sorts of things, which in some ways
Speaker 1: set the stage for deep fakes. This case, it was
Speaker 1: really just focusing on the lips and the general area
Speaker 1: around the lips, so you weren't changing the rest of
Speaker 1: the expression of the face, and you would have to,
Speaker 1: you know, keep your recording to be about the same
Speaker 1: length as whatever the film clip was, or you would
Speaker 1: have to loop the film clip over and over it,
Speaker 1: which would make it, you know, far more obvious that
Speaker 1: this was a fake. In addition, motion tracking technology was
Speaker 1: advancing over time too, and this also became an important
Speaker 1: tool in computer animation. This tool would also be used
Speaker 1: by deep fake algorithms to create facial expressions, manipulating the
Speaker 1: digital image just as it would if it were a
Speaker 1: video game character or a Pixar animated character. Typically, you
Speaker 1: need to start with some existing video in order to
Speaker 1: manipulate it. You're not actually computer generating the animation, like,
Speaker 1: you're not creating a computer generated version of whomever it
Speaker 1: is you're doing the fake of. You're using existing imagery
Speaker 1: in order to do that and then manipulating that existing imagery,
Speaker 1: So it's a little different from computer animation. In two
Speaker 1: thousand and sixteen, students and faculty at the Technical University
Speaker 1: of Munich created the face to face project that would
Speaker 1: be face the numeral two and then face and this
Speaker 1: was particularly jaw dropping to me at the time when
Speaker 1: I first saw these videos back in I was floored.
Speaker 1: They created a system that had a target actor. This
Speaker 1: would be the video of the person that you want
Speaker 1: to manipulate. In the example they used, it was former
Speaker 1: US President George W. Bush. Their process also had a
Speaker 1: source actor. This was the source of the expressions and
Speaker 1: facial movements you would see in the target So kind
Speaker 1: of like a digital puppeteer in a way, but the
Speaker 1: way they did it was really cool. They had a
Speaker 1: camera trained on the source actor and it would track
Speaker 1: specific points of movement on the source actor's face, and
Speaker 1: then the system would manipulate the same points of movement
Speaker 1: on the target actor's face in the video. So if
Speaker 1: the source actor smiled, then the target smiled, so the
Speaker 1: source actor would smile, and then you would see George W.
Speaker 1: Bush and the video smile in real time. It was
Speaker 1: really strange. They used this looping video of George W.
Speaker 1: Bush wearing a neutral expression. They had to start with
Speaker 1: that as there they're sort of zero point, and I
Speaker 1: gotta tell you, it really does look like the former
Speaker 1: president George W. Bush is having a bit of a
Speaker 1: freak out on a looping video because he keeps on,
Speaker 1: opening his mouth, closing his mouth, grimacing, raising his eyebrows.
Speaker 1: You need to watch this video. It is still available
Speaker 1: online to check out. In ten, students and faculty over
Speaker 1: at the University of Washington created the Synthesizing Obama project,
Speaker 1: in which they trained a computer model to generate a
Speaker 1: synthetic video of former US President Barack Obama, and they
Speaker 1: made it lip sync to a pre recorded audio clip
Speaker 1: from one of Obama's addresses to the nation. They actually
Speaker 1: had the original video of that address for comparison, so
Speaker 1: they could look back at that and see how they're
Speaker 1: generated one compared to the real thing. And their approach
Speaker 1: used a model that analyzed hundreds of hours of video
Speaker 1: footage of Obama speaking, and it mapped specific mouth shapes
Speaker 1: to specific sounds. It would also include some of Obama's mannerisms,
Speaker 1: such as how he moves his head when he talks
Speaker 1: or uses facial expressions to emphasize words. And watching the
Speaker 1: video and that the the real one next to the
Speaker 1: generated one is pretty strange. You can tell the generated
Speaker 1: one isn't quite right. It's not matching the audio exactly,
Speaker 1: at least not on the early versions, but it's fairly
Speaker 1: close and it might even pass casual inspection for a
Speaker 1: lot of people who weren't, like, you know, actually paying attention.
Speaker 1: Authors Morass and Alexandro defined deep fakes as quote the
Speaker 1: product of artificial intelligence applications that merge, combine, replace, and
Speaker 1: superimpose images and video clips to create fake videos that
Speaker 1: appear authentic end quote. They first emerged in seventeen and
Speaker 1: so this is a pretty darn young application of technology.
Speaker 1: One thing that is worrisome is that once someone has
Speaker 1: access to the tools, it's not that difficult to create
Speaker 1: a deep fake video. You pretty much just need a
Speaker 1: decent computer, the tools, a bit of know how on
Speaker 1: how to do it, and some time you also need
Speaker 1: some reference material, as in like videos and images of
Speaker 1: the person that you are replicating, and like the machine
Speaker 1: learning systems I've mentioned, the more reference material you have,
Speaker 1: the better. That's why the deep fakes you encounter these
Speaker 1: days tend to be of notable famous people like celebrities
Speaker 1: and politicians. Mainly there's no shortage of reference material for
Speaker 1: those types of individuals, and so they are easier to
Speaker 1: replicate with deep fakes than someone who maintains a much
Speaker 1: lower profile. Not to say that that will always be
Speaker 1: the case, or that there aren't systems out there that
Speaker 1: can accept smaller amounts of reference material. It's just harder
Speaker 1: to make a convincing version with fewer samples. But in
Speaker 1: order to make a convincing fake, the system really has
Speaker 1: to learn how a person moves. All those facial expressions matter.
Speaker 1: It also has to learn how a person sounds. Will
Speaker 1: get into sound a little bit later. But mannerisms, inflection, accent, emphasis, cadence,
Speaker 1: quirks and ticks, all of these things have to be
Speaker 1: analyzed and replicated to make a convincing fake, and it
Speaker 1: has to be done just right or else it comes
Speaker 1: off as creepy or unrealistic. Think about how impressionists will
Speaker 1: take a celebrity's manner of speech and then heighten some
Speaker 1: of it in comedic effect. You'll hear all the time
Speaker 1: with folks who do impressions of people like Jack Nicholson
Speaker 1: or Christopher Walkin or Barbara streisand people who have a
Speaker 1: very particular way of speaking. Impressionists will take those as
Speaker 1: markers and they really punch in on them. Well, a
Speaker 1: deep fake can't really do that too much, or else
Speaker 1: it won't come across as genuine. It'll feel like you're
Speaker 1: watching a famous person impersonating themselves, which is weird. Now.
Speaker 1: The earliest mention of deep fakes I can find dates
Speaker 1: to a two thousand seventeen Reddit forum in which you
Speaker 1: are shared deep faked videos that appeared to show female
Speaker 1: celebrities in sexual situations. Heads and faces had been replaced,
Speaker 1: and the actors in pornographic movies had their heads or
Speaker 1: faces swapped out for these various celebrities. Now the fakes
Speaker 1: can look fairly convincing, extremely convincing in some cases, which
Speaker 1: can lead to some people assuming that the videos are
Speaker 1: genuine and that the folks that they saw in the
Speaker 1: videos are really the ones who were in it. And
Speaker 1: obviously that's a real problem, right, I mean that this
Speaker 1: technology we've given enough reference data DEFEATA system, someone could
Speaker 1: fabricate a video that appears to put a person in
Speaker 1: a compromising position, whether it's a sexual act or making
Speaker 1: damaging statements or committing a crime or whatever. And there
Speaker 1: are tools right now that allow you to do pretty
Speaker 1: much what the face to face tool was doing back
Speaker 1: in two thousands sixteen, a program called avatar. If I
Speaker 1: just not that easy to say anyway, It can run
Speaker 1: on top of live streaming conference services like Zoom and Skype,
Speaker 1: and you can swap out your face for a celebrities face.
Speaker 1: Your facial expressions map to the computer manipulated celebrity face.
Speaker 1: It just looks at you through your webcam, and then
Speaker 1: if you smile, the celebrity image smiles, et cetera. It's
Speaker 1: like that old face to face program. It does need
Speaker 1: a pretty beefy PC to manage doing all this because
Speaker 1: you're also running that live streaming service underneath it. It's
Speaker 1: also not exactly user friendly. You need some programming experience
Speaker 1: to really get it to work. But it is widely accessible,
Speaker 1: as the source code is is open source and it's
Speaker 1: on get hubs, so anyone can get it. Samantha Cole,
Speaker 1: who writes for Vice, has covered the topic of deep
Speaker 1: fakes pretty extensively and the potential harm they can cause,
Speaker 1: and I recommend you check out her work if you're
Speaker 1: interested in learning more about that. Do be warned that
Speaker 1: Coal covers some pretty adult themed topics and I think
Speaker 1: she does great work and very important work. But as
Speaker 1: a guy who grew up in the Deep South, it's
Speaker 1: also the kind of stuff that occasionally makes me clutch
Speaker 1: my pearls, But that's more of a statement about me
Speaker 1: than her work. She does great work. I think most
Speaker 1: of us can imagine plenty of scenarios in which this
Speaker 1: sort of technology could cause mischief on a good day
Speaker 1: and catastrophe on a bad day, whether it's spreading misinformation,
Speaker 1: creating fear, uncertainty and doubt fud or by making people
Speaker 1: seem to say things they never actually said, or contributing
Speaker 1: to an ugly subculture in which people try to make
Speaker 1: their more base fantasies a reality by putting one person's
Speaker 1: head on another person's body. You know, it's not great.
Speaker 1: There are legitimate uses of the technology too, of course,
Speaker 1: you know, tech itself is rarely good or bad. It's
Speaker 1: all in how we use it. But this particular technology
Speaker 1: has a lot of potentially harmful uses, and Samantha Cole
Speaker 1: has done a great job explaining them. When we come back,
Speaker 1: I'll talk a bit more about the war against deep
Speaker 1: fakes and how people are trying to prepare for a
Speaker 1: world that is increasingly filled with media we can't really trust.
Speaker 1: But first let's take a quick break. Before the break,
Speaker 1: I mentioned Samantha Cole, who has written extensively about deep fags,
Speaker 1: and one point she makes that I think is important
Speaker 1: for us to note is that the vast majority of
Speaker 1: instances of deep fake videos haven't been some manufactured video
Speaker 1: of a political leader saying inflammatory things. That continues to
Speaker 1: be a big concern. There's a genuine fear that someone
Speaker 1: is going to manufacture a video in which a politician
Speaker 1: appears to say or do something truly terrible in an
Speaker 1: effort to either discredit the politician or perhaps instigate a
Speaker 1: conflict with some other group. There are literal doomsday scenarios
Speaker 1: in which such a video would prompt a massive military response,
Speaker 1: though that does seem like it might be a little
Speaker 1: far fetched, though heck, I don't know, considering the world
Speaker 1: we live in, maybe it's not that big of a
Speaker 1: stretch anyway. Cole's point is that so far, debt has
Speaker 1: not happened. She points out that the most frequent use
Speaker 1: for the tech either tends to be people goofing around
Speaker 1: or disturbingly using it to in her words, quote, take
Speaker 1: ownership of women's bodies in non consensual porn end quote.
Speaker 1: Cole argues that the reason we haven't really seen deep
Speaker 1: fix used much outside of these realms, apart from a
Speaker 1: few advertising campaigns, is that people are pretty good at
Speaker 1: spotting deep fix they aren't quite at a level where
Speaker 1: they can easily pass for the real thing. There's still
Speaker 1: something slightly off about them. They tend to butt up
Speaker 1: against the uncanny valley. Now, for those of you not
Speaker 1: familiar with that term, the uncanny valley describes the feeling
Speaker 1: we humans get when we encounter a robot or a
Speaker 1: computer generated figure that closely resembles a human or human behavior,
Speaker 1: but you can still tell it's not actually a person,
Speaker 1: and it's not a good feeling. It tends to be
Speaker 1: described as repulsive and disturbing, or at the very best,
Speaker 1: off putting. See also the animated film Polar Express. There's
Speaker 1: a reason that when that film came out, people kind
Speaker 1: of reacted negatively to the animation, and it's also a
Speaker 1: reason why Pixar tends to prefer to go with stylized
Speaker 1: human characters who are different enough from the way real
Speaker 1: humans look to kind of bypass uncanny valley. We just
Speaker 1: think of that as a cartoon nuts that's trying to
Speaker 1: pass itself off as being human. But while there hasn't
Speaker 1: really been a flood of fake videos hitting the Internet
Speaker 1: with the intent to discredit politicians or infuriate specific people
Speaker 1: or whatever. There remains a general sense that this is coming.
Speaker 1: It's just not here now. The sense I get is
Speaker 1: that people feel it's an inevitability, and there are already
Speaker 1: folks working on tools that will help us sort out
Speaker 1: the real stuff from the fakes. Take Microsoft, for example.
Speaker 1: There R and D division fittingly called Microsoft Research, developed
Speaker 1: a tool they called the Video Authenticator. This tool analyzes
Speaker 1: video samples and looks for signs of deep fakery. In
Speaker 1: a blog post written by Tom Bert and Eric Horvitts
Speaker 1: to Microsoft executives, they say, quote it works by detecting
Speaker 1: the blending boundary of the deep fake and subtle fading
Speaker 1: or gray scale elements that might not be detectable by
Speaker 1: the human eye. End quote. Now I'm no expert, but
Speaker 1: to me, it sounds like the video Authenticator is working
Speaker 1: in a way that's not too dissimilar to a discriminator
Speaker 1: in a generative adversarial network. I mean, the whole purpose
Speaker 1: of the discriminator is to discriminate or to tell the
Speaker 1: difference between genuine, unaltered videos and computer generated ones. So
Speaker 1: the video authenticator is looking for tailtale signs that a
Speaker 1: video was not produced through traditional means but was computer generated. However,
Speaker 1: that's the very thing that the generators in G A
Speaker 1: N systems are looking out for. So when a generator
Speaker 1: receives feedback that a video it generated did not slip
Speaker 1: past the discriminator, it then tweaks those input weights and
Speaker 1: starts to shift its approach in order to bypass whatever
Speaker 1: it was that gave away its last attempt, and it
Speaker 1: does this again and again. So the video authenticator might
Speaker 1: work well for a given amount of time, but I
Speaker 1: would suspect that in the long run, the deep fake
Speaker 1: systems will become sophisticated enough to fool the authenticator. Of course,
Speaker 1: Microsoft will continue to tweak the authenticator as well, and
Speaker 1: it will become something of a seesaw battle as one
Speaker 1: side outperforms the other temporarily, and then the balance will shift.
Speaker 1: Though there may come a time where either the deep
Speaker 1: fakes are too good and they don't set off any
Speaker 1: alarms from the discriminator, or the discriminator gets so sensitive
Speaker 1: that it starts to flag real videos and hits a
Speaker 1: lot of false positives and calls them generated videos instead.
Speaker 1: Either way, you reach a point where a tool like
Speaker 1: this no longer really serves a useful purpose, and the
Speaker 1: video authenticator will be obsolete. Now, this is something we
Speaker 1: see in artificial intelligence all the time. If you remember
Speaker 1: the good old days of capture, you know, the approving
Speaker 1: you're not a robot stuff. The stuff up we were
Speaker 1: told to do was typically type in a series of
Speaker 1: letters and numbers, and it wasn't that hard when it
Speaker 1: first started, at least not at first. That's because the
Speaker 1: text recognition algorithms of the time weren't very good. They
Speaker 1: couldn't decipher mildly deformed text because the shapes of the
Speaker 1: text felt too far outside the parameters of what the
Speaker 1: system could recognize as a legitimate letter or number. You
Speaker 1: make the number a little you know, deformed, and then
Speaker 1: suddenly the systems like, well, that doesn't look like a
Speaker 1: three to me, because it's not in the shape of
Speaker 1: a three. But over time, people developed better text recognition
Speaker 1: programs that could recognize these shapes even if they weren't
Speaker 1: in a standard three orientation, and those systems began to
Speaker 1: defeat those simple early captures that required captured designers to
Speaker 1: make tougher versions and Eventually the machines got good enough
Speaker 1: that they could match or even outperform humans, and at
Speaker 1: that point those tech based captures proved to be more
Speaker 1: challenging for people than for machines, which meant if you
Speaker 1: use them, you defeated the whole purpose in the first place.
Speaker 1: So while this escalation proved to be a challenge for security,
Speaker 1: it was a boon for artificial intelligence. And while I
Speaker 1: focused almost exclusively on the imagery of video here, the
Speaker 1: same sort of stuff is going on with generated speech,
Speaker 1: including generated speech that imitates specific voices like deep big videos.
Speaker 1: This approach works best if you have a really big
Speaker 1: data set of recorded audio, so people like movie and
Speaker 1: TV stars, news reporters, politicians, and um, you know, podcasters,
Speaker 1: we're great targets for this stuff. There might be hundreds
Speaker 1: or you know, in my case, thousands of hours of
Speaker 1: recording material to work from. Training a model to use
Speaker 1: the frequencies. Timbre, intonation, pronunciation, pauses, and other mannerisms of
Speaker 1: speech can versus in a system that can generate vocals
Speaker 1: that sound like the target, sometimes to a fairly convincing degree,
Speaker 1: and for a while. To peek behind the curtain here
Speaker 1: we at tech stuff. We're working with a company that
Speaker 1: I'm not going to name, but they were going to
Speaker 1: do something like this as an experiment. I was going
Speaker 1: to do a whole episode on it, and I had
Speaker 1: planned on crafting a segment of that episode only through text.
Speaker 1: I was not going to actually record it myself and
Speaker 1: then use a system that was trained on my voice
Speaker 1: to replicate my voice and deliver that segment on its own.
Speaker 1: I was curious if it can nail not just the
Speaker 1: audio quality of my voice, which, let's be honest, is amazing.
Speaker 1: That's sarcasm. I can't stand listening to myself, but it
Speaker 1: would also have to replicate how I actually make certain sounds,
Speaker 1: Like would it get the bit of the southern accent
Speaker 1: that's in my voice, or the way I emphasize certain words.
Speaker 1: Would it us for effect at all? Or would it
Speaker 1: just robotically say one word after the next and only
Speaker 1: pause when there was some helpful punctuation that told it
Speaker 1: to do so. Would it indicate a question by raising
Speaker 1: the pitch at the end of its sentence. Sadly, we
Speaker 1: never got far with that particular project, so I don't
Speaker 1: have any answers for you. I don't know how it
Speaker 1: would have turned out, But clearly one of the things
Speaker 1: I thought of was that it's a bit of a
Speaker 1: red flag. If you can train a computer to sound
Speaker 1: exactly like a specific person, that means you could make
Speaker 1: that person say anything you like, and obviously, like deep
Speaker 1: fake videos, that could have some pretty devastating consequences if
Speaker 1: it were at all, you know, believable or seemed realistic. Now,
Speaker 1: the company we were working with was working hard to
Speaker 1: make sure that the only person to have access to
Speaker 1: a specific voice would be the owner of that voice,
Speaker 1: or presumably the company employing that person, though that does
Speaker 1: bring up a whole bunch of other potential problems, like
Speaker 1: can you imagine eliminating voice actors from a job because
Speaker 1: you've got enough of their voice and you can just
Speaker 1: replicate it. That wouldn't be great, But even so, it
Speaker 1: was something I felt was both fascinating from a technology
Speaker 1: standpoint and potentially problematic when it comes to an application
Speaker 1: of that technology. One other thing I should mention is
Speaker 1: that the Internet at large has been pretty active in
Speaker 1: fighting deep fakes, not necessarily in detecting them, but removing
Speaker 1: the platforms from which they were being shared, Reddit being
Speaker 1: a big one, the subreddit that was dedicated to deep
Speaker 1: fakes what had been shut down, So there have been
Speaker 1: some of those moves as well. Now this is not
Speaker 1: directly against the technology, it's more against the proliferation of
Speaker 1: the uh the output of that technology. As for detecting
Speaker 1: deep fakes, it's interesting to me that people are even
Speaker 1: developing tools to detect them, because to me, the best
Speaker 1: tools so far seems to be human perception. It's not
Speaker 1: that the images aren't really convincing, or that we can
Speaker 1: suddenly detect these, you know, blending lines like the video
Speaker 1: Authenticator tool. It's rather that it's just not hard for
Speaker 1: us to spot a deep fake. Stuff just doesn't quite
Speaker 1: look right in the way that people behave in these videos.
Speaker 1: The vocals and animation often don't quite match. The expressions
Speaker 1: aren't really natural, the progression of mannerisms feels synthetic and
Speaker 1: not genuine. It just it looks off. It's that uncanny
Speaker 1: Valley thing, and so just paying attention and thinking critically
Speaker 1: can really help you suss out the fakes from the
Speaker 1: real thing. Even if we reach a point where machines
Speaker 1: can create a convincing enough fake to pass for reality,
Speaker 1: we can still apply critical thinking, and we always should. Heck,
Speaker 1: we should be applying critical thinking even when there's no
Speaker 1: doubt as to the validity of the video, because there
Speaker 1: may be enough to doubt the content of the video itself.
Speaker 1: If I listen to a genuine scam artist in a
Speaker 1: genuine video, that doesn't make the scam more legitimate. We
Speaker 1: always need to use critical thinking. What I think is
Speaker 1: most important is that we acknowledge the very real fact
Speaker 1: that there are numerous organizations, agencies, governments, and other groups
Speaker 1: that are actively attempting to spread misinformation and disinformation. There
Speaker 1: are entire intelligence agencies dedicated to this endeavor, and then
Speaker 1: there are more independent groups that are doing it for
Speaker 1: one reason or another, typically either to advance a particular
Speaker 1: political agenda or just to make as much money as
Speaker 1: quickly as possible. This is beyond doubt or question. There
Speaker 1: are numerous misinformation campaigns that are actively going on out
Speaker 1: there in the real world right now. Most of them
Speaker 1: are not depending on deep fakes, because one, deep fakes
Speaker 1: aren't really good enough to fool most people right now,
Speaker 1: and too, they don't need the deep fakes in the
Speaker 1: first place. There are other methods that are simpler that
Speaker 1: don't need nearly the processing power that work just fine.
Speaker 1: Why would you go through the trouble of synthesizing a
Speaker 1: video if you can get a better response with a
Speaker 1: blog post filled with lies or half truths. It's just
Speaker 1: not a great return on investment. So bottom line, be
Speaker 1: vigilant out there, particularly on social media. Be aware that
Speaker 1: there are plenty of people who will not hesitate to
Speaker 1: mislead others in order to get what they want. Use
Speaker 1: a critical eye to evaluate the information you encounter. Ask questions,
Speaker 1: check sources, look for corroborating reports. It's a lot of work,
Speaker 1: but trust me, it's way better that we do our
Speaker 1: best to make sure the stuff we're depending on is
Speaker 1: actually dependable. It'll turn out better for us in long run.
Speaker 1: Well that wraps up this episode of tech stuff, which yeah,
Speaker 1: I used as a backdoor to argue about critical thinking. Again,
Speaker 1: sue me, don't, don't really sue me. But I think
Speaker 1: that that's another instance where it's a really clear example
Speaker 1: where we have to use that kind of stuff. So
Speaker 1: I'm gonna keep on stressing it. And you guys are awesome.
Speaker 1: I believe in you. I think that when we start
Speaker 1: using these tools at our disposal that everybody can develop
Speaker 1: just with some practice that things will be better. We'll
Speaker 1: be able to suss out the nonsense from the real stuff,
Speaker 1: and we're all better off in the long run if
Speaker 1: we can do that. If you guys have suggestions for
Speaker 1: future topics I should cover in episodes of tech Stuff,
Speaker 1: let me know via Twitter. The handle is text stuff
Speaker 1: H s W and I'll talk to you again really soon.
Speaker 1: Tech Stuff is an I Heart Radio production. For more
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Speaker 1: Apple Podcasts, or wherever you listen to your favorite shows.