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