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Will Deepfakes Ruin the World? · Stuff You Should Know

0:01
Speaker 1: Welcome to Stuff You should know, a production of My
0:03
Speaker 1: Heart Radios How Stuff Works. Hey, and welcome to the podcast.
0:12
Speaker 1: I'm Josh Clark. There's Charles W. Chuck Bryant. Here's our
0:15
Speaker 1: brand new producer from now on, Josh Brother, Josh, Have
0:20
Speaker 1: you said your last name? Josh? We don't do that, Okay,
0:23
Speaker 1: sometimes we say Jerry's Jerry quit? She really did, but
0:27
Speaker 1: she did it like that, kind of like quiet, silent
0:30
Speaker 1: wait for her. She just stopped showing up. Yeah, Jerry
0:34
Speaker 1: didn't quit. Everyone. We don't think we're not. It's not
0:37
Speaker 1: entirely certain until I see her sitting in that chair. Then,
0:41
Speaker 1: assuming she's quit, what if, Chuck, she sent us a
0:44
Speaker 1: video of herself saying, I quit. I'm so sick of
0:48
Speaker 1: you guys. I'm done with this forever. Would you believe
0:51
Speaker 1: it then, but the lips didn't quite match up, then
0:56
Speaker 1: it would be a deep fake. It would be a
0:58
Speaker 1: deep fake. I saw a tweet. I don't remember who
1:00
Speaker 1: it was, but they were maybe Ryan Liza or somebody
1:04
Speaker 1: was complaining said why do we call these things deep fakes?
1:07
Speaker 1: And somebody school them on it. It was kind of
1:10
Speaker 1: nice to wat who he said, Ryan Liza I think
1:14
Speaker 1: he's like a CNN correspondent journalist. Uh So, first of all,
1:19
Speaker 1: we want to issue a c o A here, uh
1:23
Speaker 1: that maybe any kids shouldn't listen to this one. We're
1:27
Speaker 1: talking about some really every day dark, harmful stuff. Yeah,
1:33
Speaker 1: really despicable, gross stuff. The only thing I can think
1:36
Speaker 1: of that would be worse than covering this would be
1:38
Speaker 1: to to one on like snuff films. I kept thinking
1:43
Speaker 1: of that while I was reading this. I don't know, man,
1:45
Speaker 1: I don't want to. I don't want to pit types
1:49
Speaker 1: of despicable media against one another. But um, I think
1:52
Speaker 1: revenge porn might have a leg up on this, which
1:55
Speaker 1: this sort of is as well. Right, it's definitely a
1:58
Speaker 1: close cousin of it at least. Yeah, but this one
2:00
Speaker 1: in for kids. Uh. And I was shocked and dismayed
2:04
Speaker 1: because I didn't know about this, And when I saw
2:07
Speaker 1: it's a podcast on fake videos, I thought, well, how fine,
2:11
Speaker 1: because I love those videos of David Beckham kicking soccer
2:16
Speaker 1: balls in the cans from a hundred yards out on
2:18
Speaker 1: the beach. That's for real. Uh No, yeah, and it's
2:22
Speaker 1: just coincidence he's holding it PEPSI can up. I saw
2:25
Speaker 1: it with my own eyes. I thought that's what this
2:27
Speaker 1: is about. I was like, all those are fun. It
2:29
Speaker 1: is kind of but then I wanted to take a
2:31
Speaker 1: bath after this. I can I can understand, So we
2:36
Speaker 1: should probably start out after the CEA by saying what
2:39
Speaker 1: a deep fake is? A deep fake d E E
2:42
Speaker 1: p F A k E all one word is a
2:46
Speaker 1: type of video where somebody is saying or doing something
2:50
Speaker 1: that they never actually said or did. What you say, Okay,
2:54
Speaker 1: this is nothing new. This has been around for a while,
2:56
Speaker 1: Like people have doctored photos and videos and stuff like
2:59
Speaker 1: that for base sically as long as there's been videos.
3:02
Speaker 1: C G. I sure this is different. This is the
3:06
Speaker 1: in the same ballpark, but this is an entirely different league.
3:10
Speaker 1: Like this league plays on Saturday and Sunday afternoons, not
3:13
Speaker 1: Tuesday night, you know what I mean. Like this is
3:15
Speaker 1: something totally Just let it simmer for a little while
3:18
Speaker 1: and you'll be like, wow, it's a really good analogy. Uh,
3:20
Speaker 1: this is just it's different. It's it has a lot
3:23
Speaker 1: of the same in principle, but because they are so
3:26
Speaker 1: realistic and they're getting more and more realistic by the day,
3:29
Speaker 1: they they actually in a lot of people's minds pose
3:33
Speaker 1: a threat not just two individual people, as we'll see,
3:36
Speaker 1: but possibly to society at large, say a lot of
3:39
Speaker 1: people who are really worried about this kind of stuff. Yeah,
3:42
Speaker 1: and we're not talking about I'm assuming the fake lip
3:47
Speaker 1: reading thing that's deep fake, right or is that just
3:52
Speaker 1: no manipulation of video whatsoever? And that's just people using
3:55
Speaker 1: their voice So what that is is the Yeah, it's
3:59
Speaker 1: just somebody pretend like they're just fake lip reading and
4:02
Speaker 1: then doing a voiceover. They're not. They're not manipulating the
4:05
Speaker 1: video at all. Okay, No, they're just doing a really
4:08
Speaker 1: bad job of lip reading, which is are hysterics. They
4:12
Speaker 1: are hilarious. Those I would put those up with the
4:15
Speaker 1: the G I. Joe p S A S. Pork chop
4:18
Speaker 1: sandwiches like those are just all time classic. Can watch
4:21
Speaker 1: them anytime and still laugh. Have you ever seen the
4:23
Speaker 1: G I. Joe's action figures on like the dead road Kills.
4:30
Speaker 1: It's sad because it's a dead animal, but they'll be like,
4:33
Speaker 1: you know, a dead squirrel in the road, and someone
4:35
Speaker 1: will pose like a G. I. Joe action figure with
4:37
Speaker 1: his like foot on his head. It's like it's a trophy. Yeah, game,
4:40
Speaker 1: it's kind of funny. Yeah, I can see that. It's
4:43
Speaker 1: let's put it this way, it's as funny as you
4:44
Speaker 1: can make a picture of a dead animal and that
4:48
Speaker 1: I assume got hit by a car. You hope, But
4:51
Speaker 1: maybe they are like killing squirrels. Just after reading this,
4:55
Speaker 1: I don't doubt anything. And it makes me hate the
4:57
Speaker 1: Internet even more. All right, so let's let's get into
4:59
Speaker 1: this a little bit. Okay, Chuck, calm down. We're not
5:02
Speaker 1: allowed to have personal positions on these things, so this
5:06
Speaker 1: is totally a neutral thing. Okay. So, um, there's this
5:09
Speaker 1: really interesting Gizmoto article that that talked about the history
5:13
Speaker 1: of kind of um not necessarily deep fixed but altering videos,
5:18
Speaker 1: like presenting a doctored video as as reality. And apparently
5:23
Speaker 1: there was a long tradition of it at the beginning
5:25
Speaker 1: of cinema where people got their news from newsreels, like
5:28
Speaker 1: you actually go to a movie theater to see the
5:30
Speaker 1: news because you were just a early twentieth century yokel
5:34
Speaker 1: living in Kansas or something like that. Yeah, And after
5:38
Speaker 1: reading this bit, I thought that was a very Gizmoto
5:40
Speaker 1: way to say, here's one not so interesting fact that
5:44
Speaker 1: really has not much to do with this. Oh I
5:46
Speaker 1: love it really. I personally selected it and put it
5:48
Speaker 1: in here, and I thought it was kind of funny.
5:50
Speaker 1: I think it's great. Uh yeah, they used to fake,
5:53
Speaker 1: uh real life events and recreate them. Don't try to backpedal.
5:57
Speaker 1: And that has nothing to do with deep it does
6:01
Speaker 1: because one of the big problems are threats from deep
6:03
Speaker 1: deep fakes is it's a way of seeing what you
6:07
Speaker 1: think is news, but it's not. It's it's a sham.
6:09
Speaker 1: It's recreated. Yeah. The difference I see is they were
6:13
Speaker 1: recreating real news events and just like here, you didn't
6:17
Speaker 1: see it. So this is what it may have looked like,
6:18
Speaker 1: but they were passing it off as real. Therein lies
6:21
Speaker 1: the tragedy of all times. I thought it was a
6:24
Speaker 1: very thin GIZMOTOI whatever, we'll edit this part out Webster's
6:29
Speaker 1: defined deep fake. I like it. I put it in
6:31
Speaker 1: there specifically because I thought it was good. That's all right, Okay,
6:34
Speaker 1: so we'll take another tech Okay, why don't we just
6:37
Speaker 1: talk about deep fakes, So Chuck, let's talk about deep fakes.
6:42
Speaker 1: We can just cut there. So deep fakes actually are
6:44
Speaker 1: super new. Yeah, they and the reason they're called deep fakes.
6:48
Speaker 1: Is because in late two thousand seventeen, I think November,
6:52
Speaker 1: this guy who was a reditor, a guy who posts
6:55
Speaker 1: on Reddit at your first sign warning sign not necessarily read.
6:59
Speaker 1: It's pretty sharp and smart and got some good ideas
7:01
Speaker 1: going on as as of all the social media platforms
7:04
Speaker 1: that throw my two cents in with Reddit, but there
7:07
Speaker 1: was a redditor called deep fake d E E P
7:11
Speaker 1: F A K E all one word and he said, hey, world,
7:14
Speaker 1: look at what I figured out how to do. And
7:17
Speaker 1: he started posting pornography but with celebrities faces transposed on it,
7:24
Speaker 1: and he said, this is just my hobby, but here's
7:26
Speaker 1: how I did it. And he said that he used,
7:28
Speaker 1: I'm assuming it's a him. I don't know if it's
7:32
Speaker 1: a man or a woman. I'm gonna go with a man.
7:35
Speaker 1: UM and he said, I just used Keras and TensorFlow.
7:38
Speaker 1: And these are a couple of UM basically open source
7:42
Speaker 1: AI programs that this this guy was smart enough to
7:45
Speaker 1: figure out how to use to train to create UM
7:49
Speaker 1: these videos where you take a celebrity's face and put
7:52
Speaker 1: it on a clip from a porn movie and it
7:55
Speaker 1: looks like the celebrity is doing what you're seeing. And
8:00
Speaker 1: at first it was kind of hokey and not very
8:03
Speaker 1: it was very obviously not real. Yeah, I think the
8:07
Speaker 1: scary part was how quickly and easily it could be done. Motherboard,
8:13
Speaker 1: who we we used to write for every now and then.
8:15
Speaker 1: Remember that I tried to forget. They tried to forget
8:18
Speaker 1: for sure. Yeah, if I hit us up, what like,
8:21
Speaker 1: I feel like seven or eight years ago, I'm trying
8:23
Speaker 1: to forget and you're making it really hard. Said you
8:26
Speaker 1: guys want to write some blogs for Motherboard. He said, sure,
8:28
Speaker 1: so we did. We did. We wrote ten. Yeah, you
8:31
Speaker 1: can probably go find those on the internet if you
8:32
Speaker 1: want to learn how to they drive a stick shift
8:34
Speaker 1: or something. The fine people that motherboards scrubbed those from
8:37
Speaker 1: the internet, let's hope. So so Uh, this deep fake
8:41
Speaker 1: character figures this out. Another guy released a downloadable desktop
8:45
Speaker 1: software that said here, you can do this awful thing too. Right,
8:50
Speaker 1: within like two months of of deep Fake coming out
8:53
Speaker 1: and saying look what I did and here's how I
8:54
Speaker 1: did it, somebody said that's a really good idea. I'm
8:57
Speaker 1: going to turn it into an app and make it
8:59
Speaker 1: give it to everybody. That's right, And now, uh, people.
9:03
Speaker 1: Can you know at this time, this was a very
9:05
Speaker 1: short time ago, people, and it's really come a long
9:07
Speaker 1: way in the past whatever, not even two years, because
9:11
Speaker 1: there's a late right early two eighteen when it really
9:15
Speaker 1: first popped up. Yeah. So this thing was downloaded a
9:17
Speaker 1: hundred thousand times in the first month alone, and some
9:21
Speaker 1: people used it for fun stuff, like uh, putting Nick
9:24
Speaker 1: Cage in movies he wasn't in. Yeah, those are called
9:27
Speaker 1: dirt fakes. Yeah, they've all got fun names, don't they. Dude.
9:32
Speaker 1: Nicholas Cages Yoda is patently objectively hilarious. I didn't like
9:37
Speaker 1: that one. I thought the I don't know, the rages
9:41
Speaker 1: of all stark thing was interesting, I guess, but none
9:44
Speaker 1: of them made me laugh. Like maybe I just don't
9:47
Speaker 1: have that kind of sense of humor. Yeah, but yeah,
9:50
Speaker 1: I did. Never was like, oh my god, that's hysterical.
9:53
Speaker 1: It's Nick Cage's face. I understand. I understand where you
9:55
Speaker 1: come from. I don't think I was, like, you know,
9:57
Speaker 1: in stitches or anything like that. But it's pretty great. Okay, Okay,
10:01
Speaker 1: it's just not my thing. You're not a Gizmoto reader,
10:04
Speaker 1: are you. No, none of this is my thing. But
10:06
Speaker 1: that doesn't mean we can't report on it. However, since
10:09
Speaker 1: it started happening that it became pretty clear pretty quickly
10:13
Speaker 1: that this could be a bad thing in the future,
10:16
Speaker 1: and not just for putting your ex girlfriend's face on
10:22
Speaker 1: a sex video, you know, saying look what she did.
10:26
Speaker 1: It could you could put a world leader up there
10:28
Speaker 1: and uh really cause a lot of problems. Yes, hypothetically
10:33
Speaker 1: you could. And that's that's really as we'll see this,
10:37
Speaker 1: this new technology, this deep fake technology, it poses at
10:41
Speaker 1: least two risks to immediately obvious risks, and they're hyper
10:46
Speaker 1: individualized and hyper macro social. That's risks, but they both
10:52
Speaker 1: stem from the same thing, from the same route or
10:56
Speaker 1: same seat. To keep the metaphor going and on track,
10:59
Speaker 1: that's so, let's talk about the technology behind this, because
11:03
Speaker 1: this stuff is just totally fascinating. Surely you agree it
11:07
Speaker 1: is AI. It was created by a guy named Ian Goodfellow,
11:11
Speaker 1: just this particular type of AI. He didn't make the
11:13
Speaker 1: deep fake stuff, no, no, no. But basically what this
11:18
Speaker 1: model you know, everyone knows AI is basically when you
11:21
Speaker 1: teach a machine to start teaching itself, starts learning on
11:25
Speaker 1: its own, which is a little creepy. Um. But the
11:29
Speaker 1: model that they're using these days is called artificial neural
11:31
Speaker 1: net which is uh, machine learning. And basically what they've
11:35
Speaker 1: done in this case is all you have to do
11:37
Speaker 1: is show something a lot of data for it to
11:40
Speaker 1: start to be able to recognize that data when you
11:43
Speaker 1: aren't showing it that data. Yeah, and it learns on
11:45
Speaker 1: its own. What makes the classic example is um AI
11:50
Speaker 1: that can pick out pictures of cats, and it's easy enough,
11:54
Speaker 1: but you don't tell the AI, here's what a cat is.
11:57
Speaker 1: Find pictures of cats in this data set. It's here's
12:00
Speaker 1: a bunch of stuff, and figure out what a cat is,
12:03
Speaker 1: and they get really good at picking it out. You
12:06
Speaker 1: can also turn it the opposite way, once you have
12:08
Speaker 1: an AI trained on identifying cats and get it to
12:11
Speaker 1: produce pictures of cats, but they're usually terrible and often
12:17
Speaker 1: very very bizarre, like anyone would look at it and
12:20
Speaker 1: be like a human didn't make this. It's just off
12:23
Speaker 1: in some really obvious ways. And what you and Goodfellow
12:27
Speaker 1: figured out was a way around that problem. Yeah. So, uh,
12:31
Speaker 1: I'm not sure I agree with his wording here, um,
12:34
Speaker 1: but we'll we'll say what he calls it, uh, he
12:36
Speaker 1: set up two teams and one is a generator and
12:40
Speaker 1: one is a discriminator, and he calls it generative adversarial network.
12:46
Speaker 1: So basically his contention is that these two are adversarial.
12:49
Speaker 1: I saw it more as like a managerial in nature. Okay, bureaucratic, Yeah,
12:57
Speaker 1: I mean, isn't that what it felt like you or
12:59
Speaker 1: the the scriminer is like, yeah, I'm gonna need you
13:02
Speaker 1: to come in on Saturdays. It kind of felt like,
13:05
Speaker 1: So you've got these two networks and they're both trained
13:08
Speaker 1: on the same data set, but the generator is the
13:10
Speaker 1: one that's producing these fake cats, and then there's a
13:15
Speaker 1: discriminator or what I like to call a manager saying
13:18
Speaker 1: these look good. These don't look so good? Right. The
13:21
Speaker 1: other way, the way that good Fellow has proposed it,
13:23
Speaker 1: is that the discriminator is going through and looking at
13:27
Speaker 1: these generated pictures and trying to figure out if it's
13:31
Speaker 1: real or if it's from the data, or if it's
13:33
Speaker 1: fake the generator created it, or if the or if
13:37
Speaker 1: it comes from the data set, And based on the
13:40
Speaker 1: feedback that the manager gives the generator um the generator
13:45
Speaker 1: is going to adjust as parameters so that it gets
13:47
Speaker 1: better and better at putting out more realistic pictures of cats. Yeah.
13:52
Speaker 1: I don't get the adversarial part unless it gets mean
13:54
Speaker 1: and how it delivers that message the way the reason
13:57
Speaker 1: they call it ever sincere is I saw it, but like, um,
14:00
Speaker 1: it's like an art forger and detective and the art
14:05
Speaker 1: forgers putting out or an appraiser is a better way
14:08
Speaker 1: to put it. No art forgers putting out forged art,
14:11
Speaker 1: and the appraisers like this is fake, this is fake,
14:13
Speaker 1: this is fake. Well, I'm not sure about this one.
14:16
Speaker 1: I don't know if this one is fake. This is real,
14:18
Speaker 1: this is real, this is real. And then at that point,
14:21
Speaker 1: the generator has become adept at fooling an AI that's
14:26
Speaker 1: trained to identify pictures of cats creating pictures of cats
14:29
Speaker 1: that don't exist. Okay, it's adversarial. They're trying, the generators
14:34
Speaker 1: trying to fool the discriminator, and the discriminators trying to
14:38
Speaker 1: thwart the generator. That's the adversarial part. But in the
14:41
Speaker 1: end they're really on the same team. Yeah, okay, I
14:44
Speaker 1: guess that's where it loses me. You have a really
14:46
Speaker 1: positive image of corporate America. What is that the two
14:50
Speaker 1: with anything. The managers on the same team as everybody.
14:54
Speaker 1: Come on, get on board, Get on the trolley, Josh. So,
14:57
Speaker 1: if you want to look up Mona Lisa talking, there
15:00
Speaker 1: was a video this year from Samsung that showed how
15:03
Speaker 1: you could do this, and all the stuff is on YouTube.
15:07
Speaker 1: If you want to see Nick Cage as Indiana Jones,
15:10
Speaker 1: which is pretty funny, or Yoda, which is hilarious, or
15:13
Speaker 1: if you want to see Mona Lisa talking, it looks
15:16
Speaker 1: it looks fairly realistic, like hey, they brought that painting
15:19
Speaker 1: to life, right yeah, yeah, And if you scroll down
15:22
Speaker 1: a little bit, they did one with Marilyn Monro too,
15:24
Speaker 1: they brought her to life. He did interesting. Well, I
15:28
Speaker 1: mean this is like just set up for for TV commercials. Yes,
15:33
Speaker 1: they've already done stuff like this, right, This is like
15:36
Speaker 1: fredis Stared dancing with a dirt devil or whatever that was.
15:40
Speaker 1: What this could bring is creating entirely new movies and
15:45
Speaker 1: bringing back dead actors and actresses or I guess as
15:49
Speaker 1: actors now these days. Right, Uh well, I mean you've
15:53
Speaker 1: seen some of them. Are you talking about the the
15:55
Speaker 1: aging people are just creating, like bringing back someone that's
15:59
Speaker 1: been long dead. No, No, I'm saying like you call
16:02
Speaker 1: actors and actresses just actors these days. Oh that part.
16:05
Speaker 1: Uh you can do whatever you want, okay, Uh, motion
16:09
Speaker 1: picture performers, maybe even television performers, but bringing them back
16:13
Speaker 1: and giving them like they could star in an entirely
16:17
Speaker 1: because it's so realistic in life. Yeah. Um, they're not
16:21
Speaker 1: at that point yet because they're just now getting to
16:24
Speaker 1: where the d aging looks decent depending on who it is. Uh,
16:28
Speaker 1: Like the Sam Jackson stuff and Captain Marvel, Uh, my
16:32
Speaker 1: friend looked pretty good. It looked amazing. Yeah. And this
16:36
Speaker 1: Will Smith stuff in this new angry movie looks really good.
16:39
Speaker 1: What's that one? He's an angry movie where he plays
16:42
Speaker 1: a some sort of assassin that uh Aladdin? Yeah, that's it.
16:49
Speaker 1: Have you seen that? It was good? No? I have
16:51
Speaker 1: no interest, um to go back and kill with the
16:55
Speaker 1: younger version of himself or the young ones trying to
16:57
Speaker 1: kill the older one. Is what it is. Man. It's
16:59
Speaker 1: a lot like Looper, uh sort of, but it looks
17:02
Speaker 1: pretty good. Like it looks like young Will Smith. Slightly uncanny,
17:07
Speaker 1: but not as bad as I think. Some people are
17:08
Speaker 1: easier than others, like the Michael Douglas stuff and aunt
17:11
Speaker 1: Man and the marble stuff is kind of creepy looking.
17:14
Speaker 1: I haven't seen that. I mean I've seen parts of it,
17:16
Speaker 1: but I didn't notice that they were d aging Michael Douglas.
17:18
Speaker 1: They took Michael Douglas back in scenes to like the seventies.
17:22
Speaker 1: It's just like it doesn't look great. But anyway, that's
17:25
Speaker 1: sort of off track, um, but not really. I mean,
17:28
Speaker 1: it's kind of similar type of stuff. I guess, no
17:30
Speaker 1: more than that Gizmoto article to start. Yeah, that's a
17:32
Speaker 1: good point, um, But the whole reason we should point
17:36
Speaker 1: out that people are doing this stuff with celebrities and
17:40
Speaker 1: stuff like that, it's just because there's more data out there.
17:42
Speaker 1: It's a lot easier when you have a gazillion pictures
17:45
Speaker 1: of Brad Pitt on the internet to do a fake pit. Yeah,
17:49
Speaker 1: because the data set that the AI is trained on
17:52
Speaker 1: is just more and more robust. And the more pictures are,
17:56
Speaker 1: the more angles the AI has seen Brad Pitt, you know,
17:59
Speaker 1: look round at, and so can recreate this these faces
18:03
Speaker 1: because the AI seem like every possible pose or expression
18:08
Speaker 1: or whatever Brad Pitts ever made. But the thing is
18:11
Speaker 1: that mona Lisa and Marilyn Monroe. Thing that Samsung showed,
18:15
Speaker 1: They showed that you could make a pretty convincing deep
18:19
Speaker 1: fake with just one picture, one pose, right, So that's
18:23
Speaker 1: a big deal. But again, the bigger the data set,
18:26
Speaker 1: the better. And that's why, like you said, celebrities and
18:30
Speaker 1: and UM world leaders were the earliest targets. But over time,
18:35
Speaker 1: with the advent of other software and the fact that
18:38
Speaker 1: people now post tons of stuff about themselves and pictures
18:42
Speaker 1: of themselves on social media, um, it's become easier and
18:46
Speaker 1: easier to make a deep fake video of anybody. There's
18:51
Speaker 1: like holes, their software that scrapes social media accounts for
18:55
Speaker 1: every picture and video that's been posted ants, right, um distinction, Yeah,
19:00
Speaker 1: for sure. Um there's and then there's um other other
19:04
Speaker 1: sites and other apps that say, oh this this picture
19:07
Speaker 1: of this person. You're targeting, your your classmate or whatever.
19:11
Speaker 1: They probably have a pretty good match with this porn star,
19:14
Speaker 1: So go find videos of this porn star. And then
19:17
Speaker 1: the next thing, you know, you run it through that app,
19:19
Speaker 1: that deep fake app came out with, and you've got
19:21
Speaker 1: yourself a deep fake video and you've officially become a
19:25
Speaker 1: bad person. All right, that's a good place to take
19:28
Speaker 1: a break, and uh, we'll talk more about these bad people,
19:32
Speaker 1: right for this that's what sk as good should all right,
19:54
Speaker 1: So you mentioned before the break, Uh, this person's face
19:58
Speaker 1: that I just stole off the internet fits this porn
20:01
Speaker 1: actors body, which is a it's a consideration if you're
20:06
Speaker 1: making that, because to look right, that has to they
20:10
Speaker 1: have to bear a passing resemblance. I think that's right. Okay,
20:13
Speaker 1: So I was just about to say, so, yeah, So
20:16
Speaker 1: what they're doing now is they're browsing these applications with
20:20
Speaker 1: facial recognition software to make this a lot easier. And
20:24
Speaker 1: that's what most of this is about, is like, let's
20:27
Speaker 1: just see how easy we can make this and how
20:30
Speaker 1: how much we can democratize this where any shmoke can
20:33
Speaker 1: take any single photo and do the worst things possible
20:36
Speaker 1: with it, but also how convincing they've become as well. Yeah,
20:42
Speaker 1: I mean, so it's another big change. It's looking better
20:44
Speaker 1: and better, quicker and quicker, which is pretty scary. Did
20:47
Speaker 1: you see the Obama one. Had it not been for
20:51
Speaker 1: had it not been for Jordan's Peel's voice obviously being
20:55
Speaker 1: not Obama, I would have been like, Wow, this is
20:57
Speaker 1: really convincing. Really, Yeah, see how didn't think the lips
21:00
Speaker 1: matched up at all? Oh? I thought it looked pretty close. Yeah.
21:03
Speaker 1: So what we're talking about is Jordan Peel did a
21:06
Speaker 1: basically a demonstration video to raise awareness about how awful
21:10
Speaker 1: this is by doing it himself, and did a video
21:13
Speaker 1: of Obama like you know, referring to Trump as a
21:17
Speaker 1: curse word, a dipstick and basically saying like, hey, this
21:21
Speaker 1: is Obama and what you know people are doing. He
21:24
Speaker 1: basically is describing what's happening as you're watching it. And
21:28
Speaker 1: I thought it look kind of fake. He's describing a
21:29
Speaker 1: deep fake through deep fake in Jordan Peel, and he
21:33
Speaker 1: did it in conjunction with BuzzFeed and another production company.
21:35
Speaker 1: But in their defense, they were making this in like
21:38
Speaker 1: early two thou eighteen, like April two eighteen, and since then,
21:42
Speaker 1: even more technologies come out that is dedicated to matching
21:46
Speaker 1: the movement of the mouth to whatever words you want
21:49
Speaker 1: the person to say. Yeah, and you can also like
21:53
Speaker 1: use only parts of it, so it's even more convincing.
21:56
Speaker 1: So like if Obama had a lead in that actually worked,
22:00
Speaker 1: you could just keep that in there and then take
22:02
Speaker 1: out certain words, and you can manipulate it however you
22:05
Speaker 1: want to write, and the AI can go through and
22:07
Speaker 1: find like phone names and stuff like that to make
22:10
Speaker 1: the new words that the person never said. It's it's
22:14
Speaker 1: becoming extremely easy. Let's just put it like this. It's
22:17
Speaker 1: becoming extremely easy, and it's um widely available for anybody
22:21
Speaker 1: to make a video of somebody doing something or saying
22:25
Speaker 1: something that they never did or never said, and to
22:27
Speaker 1: make it convincing enough that you may believe it at first. Yeah, which,
22:32
Speaker 1: like we said that, you know, the obvious scariest implications
22:36
Speaker 1: that aren't just of the personal variety are in politics,
22:40
Speaker 1: when you could create real fake news that actually put
22:45
Speaker 1: people in jeopardy. They were put the entire world in
22:48
Speaker 1: jeopardy by like announcing a nuclear strike or something like that. Right, Yeah,
22:52
Speaker 1: Marco rubio In I can't remember when it was, but um,
22:56
Speaker 1: within the last year or two basically said that deep
22:59
Speaker 1: fakes or the modern equivalent of a nuclear bomb, that
23:03
Speaker 1: you could threaten America the degree with the deep fake.
23:08
Speaker 1: I think that is a little hyperbolic for sure, And
23:10
Speaker 1: we're not the only ones. Yeah, there are other people
23:12
Speaker 1: that say people that know what they're talking about not
23:14
Speaker 1: just you know, shlubs like us, but other people that say, like, hey, listen,
23:19
Speaker 1: this is probably not like a nuclear bomb going off. Um,
23:23
Speaker 1: we should keep our on it. But there are other
23:26
Speaker 1: bigger fish to fry when it comes to stuff like this,
23:28
Speaker 1: for sure. And then there are other people who are saying,
23:30
Speaker 1: well there are that's not to discount like the real
23:34
Speaker 1: problem it composed right, Like, we're already in a very
23:38
Speaker 1: polarized position in this country. Um. So the idea of
23:43
Speaker 1: having realistic, um, indistinguishable from reality videos of like world
23:49
Speaker 1: leaders or senators or whoever saying whatever is not going
23:53
Speaker 1: to help things at all. It's not going to bring
23:56
Speaker 1: everyone together like look at this hilarious deep fake. It's
23:59
Speaker 1: going to be like you look, um, and it's just
24:02
Speaker 1: going to erode that trust that that is necessary for
24:05
Speaker 1: a democracy to thrive. Um. And to to take into
24:10
Speaker 1: his logical conclusion, this one researcher put it like this, like,
24:15
Speaker 1: eventually we're going to lose our ability to agree on
24:20
Speaker 1: what is shared objective reality. And at that point, what
24:24
Speaker 1: we would face is what's called the death of truth,
24:29
Speaker 1: like there is no such thing anymore. And in one
24:32
Speaker 1: on one hand, that's horrible. It's a horrible idea, the
24:35
Speaker 1: idea that nothing's real because there's such a thing as
24:40
Speaker 1: deep fakes, and anybody could could make something like this.
24:43
Speaker 1: But on the other hand, you can kind of say,
24:46
Speaker 1: you can't engage his Yoda right exactly. On the other hand, though,
24:50
Speaker 1: you can say, the fact that people know that deep
24:53
Speaker 1: fakes are out there means that it's gonna be easier
24:58
Speaker 1: and easier to be like, that'sbviously not real. It's just
25:01
Speaker 1: too unbelievable. So it may actually make us more discriminating
25:06
Speaker 1: and more discerning of the news than we are today. Yeah,
25:09
Speaker 1: that's the only thing that salvaged my brain from this
25:12
Speaker 1: track of talking about this today was like, well, we'll
25:16
Speaker 1: go tell our listeners at least be on the lookout,
25:21
Speaker 1: be wary, take everything with a grain of salt, because
25:24
Speaker 1: we're already in a place where, like, you don't even
25:26
Speaker 1: need some deep fake video like it's happened all over
25:29
Speaker 1: the place. You can see a uh, something that's photoshopped
25:32
Speaker 1: or a real photo that someone just writes a false
25:36
Speaker 1: story about. Yeah, that's a good one. Um, you can
25:38
Speaker 1: just come up with a false narrative from a picture
25:41
Speaker 1: when there's a guy on the street and he's laying
25:42
Speaker 1: there bleeding, and you can just say, uh, this person
25:45
Speaker 1: was attacked yesterday by a group of angry Trumpers or
25:50
Speaker 1: an Antifa on the other side, and it'll get passed
25:54
Speaker 1: around twenty million times, and then the retraction gets seen
25:58
Speaker 1: by people exactly. That's where that's not a deep fake. No,
26:02
Speaker 1: that's just that's low hanging fruit. That's a low five fake.
26:05
Speaker 1: Imagine inserting into that and this is what we're talking about,
26:07
Speaker 1: into that climate like video where you're looking at the
26:11
Speaker 1: person seeing and seeing with your own eyes what they're saying.
26:15
Speaker 1: And a lot of people who aren't, like I thought
26:17
Speaker 1: the Obama video looked pretty fake, you thought it looked
26:19
Speaker 1: pretty real. Everyone's eye is different and ear is different.
26:25
Speaker 1: Like a lot of people will believe anything they see
26:28
Speaker 1: like this, right, And we'll we'll we'll talk about like
26:31
Speaker 1: how to discern deep fakes in a second. But we're
26:34
Speaker 1: getting to the point people seem to be in wide
26:37
Speaker 1: agreement that very soon it will be up to digital
26:42
Speaker 1: forensic scientists to determine whether a video is authentic or not.
26:47
Speaker 1: And that's all that's because you or I will not
26:50
Speaker 1: be able to distinguish it from reality. Yeah, and I
26:52
Speaker 1: imagine that every country will have their own team that
26:56
Speaker 1: will be hard at work doing that stuff, and by
26:58
Speaker 1: will already yeah, has since at the end of two
27:02
Speaker 1: thousand seventeen. Yeah, or at least they're scrambling to catch up.
27:07
Speaker 1: Because when the video comes out of um, you know,
27:12
Speaker 1: the leader of North Korea saying we want to drop
27:15
Speaker 1: bombs on America at two o'clock this afternoon, that's going
27:19
Speaker 1: to send our DARPA team scrambling to try and disprove
27:22
Speaker 1: this thing before we push the button. Right. It's like
27:25
Speaker 1: war games, it is, but way way worse. Yeah. So,
27:28
Speaker 1: just to reiterate one more time, the one thing that
27:31
Speaker 1: you and I can do, and the one thing that
27:33
Speaker 1: you guys out there listening can do to keep society
27:35
Speaker 1: from eroding is to know that deep fake videos are
27:39
Speaker 1: very real, and just about anybody with enough computing power
27:43
Speaker 1: and patients to make one can make one. And the
27:47
Speaker 1: very fact that those things exist should make you question
27:50
Speaker 1: anything you see or hear with your own eyes that
27:53
Speaker 1: seems unbelievable or sensational. Unfortunately, I think the stuff you
27:56
Speaker 1: should know crowd is pretty savvy, so we're sort of
27:59
Speaker 1: pre into the choir here. Yeah, but maybe they can
28:02
Speaker 1: go preach to their older relatives on Facebook or exactly
28:06
Speaker 1: take this to Thanksgiving dinner and just explain it to folks. Uh,
28:11
Speaker 1: we should talk about porn a little more. Should we
28:13
Speaker 1: take a break first? Sure? Are you okay with that? Yeah?
28:17
Speaker 1: I feel bad now? No? Okay, Well we'll wait and
28:20
Speaker 1: talk about porn in about sixty seconds, ask as good?
28:37
Speaker 1: Shouldn't all right, Chuck? You promised talking about porn? Yeah? This? Uh?
28:44
Speaker 1: In this research, it says one of the defenses people
28:47
Speaker 1: make in favor of deep pig porn is it it
28:48
Speaker 1: doesn't actually harm anyone. Is anyone actually saying that? Yeah?
28:52
Speaker 1: A lot of people who I shouldn't say a lot
28:54
Speaker 1: I've seen at least quote from people who make this
28:57
Speaker 1: stuff saying like this is a this is the media
29:00
Speaker 1: drumming up a moral panic, Like what's the what's the
29:03
Speaker 1: problem here? What's the issue? It's not like where it's
29:05
Speaker 1: not like they're going and hacking into like a star's
29:10
Speaker 1: iCloud account, getting naked pictures of them and then distributing
29:13
Speaker 1: that and like this is really a private naked picture
29:17
Speaker 1: of a celebrity people into thinking they've done that to them.
29:21
Speaker 1: I think that they would say they're just creating some
29:23
Speaker 1: fantasy thing that's not even real. It doesn't exist. I'm
29:27
Speaker 1: not defending it. I'm just telling you what the other
29:29
Speaker 1: side is saying. Yeah. Well, and that's the perfect example
29:32
Speaker 1: of why these are very bad people, because it is
29:35
Speaker 1: it is harmful, um to everyone involved, to the person
29:38
Speaker 1: whose face you're using, to the adult film actor who
29:43
Speaker 1: did a scene wants credit, and well, yeah, I mean
29:47
Speaker 1: it's regardless of how you feel about that stuff, someone
29:50
Speaker 1: did something and got paid a wage to do so,
29:53
Speaker 1: and now it's being ripped off and there are real
29:56
Speaker 1: people's faces involved in real bodies involved in real lives.
30:00
Speaker 1: It's you know, it's not a moral panic. But you know,
30:04
Speaker 1: it's not like we need to march this to the
30:05
Speaker 1: top of Capitol Hill right now. Well that's funny because
30:08
Speaker 1: Congress held hearings on it this year. Well yeah, but
30:10
Speaker 1: I have a feeling it's a little bit more about
30:13
Speaker 1: the political ramifications than putting your ex girlfriend's face on
30:17
Speaker 1: a porn body. Oh yeah, yeah, yeah, I see what
30:19
Speaker 1: you mean. Although they could do you know, they could
30:21
Speaker 1: put your your governor's face on a porn body and
30:25
Speaker 1: get them removed from office. You know, this video was
30:28
Speaker 1: just dug up. Look at look at this, Uh, look
30:31
Speaker 1: at your governor. Yeah, look what he's doing. For sure,
30:34
Speaker 1: But even take that down to the more the less
30:37
Speaker 1: political level, like you were saying, you could ruin somebody's
30:40
Speaker 1: marriage if it was shaky or on the rocks before. Sure,
30:43
Speaker 1: Hey here's the sex tape of your husband or your wife.
30:45
Speaker 1: You know, yes, blackmails another one too. Um, there's a
30:50
Speaker 1: lot of ramifications of this, and it seems like the
30:52
Speaker 1: more you dig into it, the more becomes clear that
30:55
Speaker 1: really the big threat from this is to the individual
30:59
Speaker 1: whose face is used on the deep fake porn. Right,
31:02
Speaker 1: they could do a video of us holding hands walking
31:04
Speaker 1: down the street, right, or they could just use that
31:08
Speaker 1: video of us doing that, the one that exists around
31:10
Speaker 1: And I'm glad you picked up that one. Um. There
31:14
Speaker 1: was just jeez, just a couple of weeks ago because
31:18
Speaker 1: I saw this one when I google this undernews, so
31:21
Speaker 1: it's very recently. There was an app that we won't
31:24
Speaker 1: name that's undressed. Basically, what you could do is just
31:27
Speaker 1: take a picture of any woman, plug it into this app,
31:31
Speaker 1: and it would uh show her, um, what she would
31:35
Speaker 1: look like nude, not her body, but it would just
31:38
Speaker 1: do it so fast and so realistically that you could
31:41
Speaker 1: nude up some woman with a touch of a button. Yeah,
31:44
Speaker 1: and like it would it would replace her clothes in
31:47
Speaker 1: the picture with nude clothes. Right. So the birthday suit right,
31:53
Speaker 1: birthday suit right, that's what I was looking And it's
31:54
Speaker 1: just as awful as you think. Uh. And the creator
31:57
Speaker 1: actually even shut it down within like three or four days. Yeah,
32:01
Speaker 1: but like what was this guy thinking, like this is
32:03
Speaker 1: a great idea. I'm like, oh, people have a problem
32:05
Speaker 1: with this, Well I'll shut it down. Like really, in
32:09
Speaker 1: his defense, he's probably like fourteen, Well, I guess that's
32:14
Speaker 1: a good point. Uh. Even if you plugged in a
32:16
Speaker 1: picture of a man, it would show a woman's nude body.
32:18
Speaker 1: And you know what that means. That means that And
32:21
Speaker 1: the person who created this app says, well, I just
32:23
Speaker 1: did that because there are way more pictures of naked
32:26
Speaker 1: women on the internet, and I was gonna do a
32:28
Speaker 1: man's version, but I had to go to baseball practice
32:30
Speaker 1: and never got a chance to. Yeah. That's uh, that's
32:34
Speaker 1: pretty amazing. And of course that person is anonymous, right
32:37
Speaker 1: as far as I know. Yeah, which means that they
32:40
Speaker 1: really must be four Team because they weren't unmasked on
32:43
Speaker 1: the internet. Despite the outrage against this, You're probably right.
32:46
Speaker 1: I wonder that it's just farmer Bro. That guy justaed him.
32:50
Speaker 1: He's still in jail. Is he really good? So that's
32:54
Speaker 1: a good um segue into what you can do if
32:58
Speaker 1: this happens to you, Right, there's a lot of outrage,
33:03
Speaker 1: Congressman against There's a lot of outrage against this kind
33:06
Speaker 1: of thing on the internet. So if this, if you
33:11
Speaker 1: are targeted and you end up in a deep fake
33:14
Speaker 1: porn clip or video or whatever, um, you could drum
33:20
Speaker 1: up some moral outrage on the Internet or go to
33:24
Speaker 1: the site that is being hosted on directly and say, hey,
33:28
Speaker 1: I know this is messed up. You know this is
33:29
Speaker 1: messed up. Please take this down. I didn't consent to this.
33:32
Speaker 1: This is an invasion of my privacy. Get rid of
33:35
Speaker 1: this video. Porn websites are good about that, actually, yep,
33:38
Speaker 1: they don't want that stuff on there. No, And it's
33:40
Speaker 1: not just porn websites like um, porn Hub, Reddit, giffy
33:45
Speaker 1: Um some other sites have banned all kinds of deep
33:49
Speaker 1: fake videos and apparently giffy I think it's giffy g
33:53
Speaker 1: y f y idea. I don't either. I've never heard
33:55
Speaker 1: of it until I started researching this. But this site
33:58
Speaker 1: actually created an AI that's trained to spot deep fig
34:02
Speaker 1: videos and remove them from the site, which is a
34:04
Speaker 1: big tool that they need to be sharing with everybody else.
34:08
Speaker 1: But if you can contact the site and say, hey, man,
34:11
Speaker 1: take this down, this is me, they will probably take
34:14
Speaker 1: it down just because of the everybody knows this is
34:16
Speaker 1: really messed up. Yeah, they got enough. They have plenty
34:20
Speaker 1: of videos to work for from that are real. They
34:23
Speaker 1: don't need this stuff. But there's no laws that say
34:26
Speaker 1: they have to take it down, are there? Well not yet. Uh.
34:29
Speaker 1: There's this guy, Henry Fred He studies digital forensics at
34:33
Speaker 1: Dartmouth and they are hard at work. Like again, this
34:37
Speaker 1: just started, you know, very recently, so all of this
34:40
Speaker 1: stuff they're just like scrambling to get ahead of as
34:44
Speaker 1: far as uh sniffing this stuff out. Well, the whole
34:47
Speaker 1: world was caught off guard by this. Oh yeah, like
34:49
Speaker 1: this guy just wanted, hey, look what I can do,
34:51
Speaker 1: and I'm going to change the world here. Nick Kage
34:53
Speaker 1: is so funny. Oh wait, what is Nick kide doing
34:57
Speaker 1: as yoda. Oh my god, So profession nials, there are
35:00
Speaker 1: some pretty uh easy to spot things if you're a pro,
35:04
Speaker 1: unless it's just really bad. Um, your average layman can
35:07
Speaker 1: spot those. But if you're a pro, you're gonna look
35:09
Speaker 1: for like bad compression, um stuff that like you know,
35:14
Speaker 1: lighting that looks off. Yeah. Not blinking is a big one. Yeah,
35:19
Speaker 1: like Michael Caine don't blink. Maybe he's just a big
35:24
Speaker 1: deep fake his whole career. Man, that'd be something. Um,
35:28
Speaker 1: sound is a big thing, like wa wait, hold on, hold,
35:30
Speaker 1: I want to say why blinking is not a thing.
35:32
Speaker 1: Oh sure, because it's fascinating. I mean it probably wasn't
35:34
Speaker 1: to you because you didn't like that gives motor article.
35:37
Speaker 1: But um, the reason why not blinking is the thing
35:41
Speaker 1: in deep fake is because deep fake AI that is
35:45
Speaker 1: trained on data sets probably are being shown photos of
35:49
Speaker 1: somebody not blinking, so they don't learn that people blink,
35:53
Speaker 1: so they don't The AI doesn't know to add blinking
35:56
Speaker 1: when it renders the new face on this video. But
35:59
Speaker 1: all I can do is just say, all right, well
36:01
Speaker 1: now then we'll program it to blink. Right. That's the
36:04
Speaker 1: big problem, Chuck. It's like everything they can spot. In fact,
36:06
Speaker 1: when they list out all the things like look for
36:09
Speaker 1: blocking this, compression, fixed pattern noise, I'm sure there's some
36:13
Speaker 1: deep fakir that's like check check, check, Thanks for the
36:16
Speaker 1: list of stuff we need to work on, and they're there.
36:18
Speaker 1: It's still maybe, like you were saying, at a point
36:21
Speaker 1: where possibly you or I could look at a shoddy
36:24
Speaker 1: video and be like, yeah, I see this this and
36:27
Speaker 1: this is a little wrong, like there is a little
36:29
Speaker 1: bit of compression um relic or remnants or whatever, like
36:33
Speaker 1: they're not blinking the shadows off a little bit. But
36:35
Speaker 1: there's also plenty of videos where you do need to
36:37
Speaker 1: be like a digital forensic scientists to to find them
36:41
Speaker 1: or an AI to find it. Yeah, you can also
36:43
Speaker 1: use your ear holes because you know, look at the
36:47
Speaker 1: room that the person is in and would it sound
36:49
Speaker 1: like that in a room like that, And that's a
36:51
Speaker 1: good One of the things audio specialists look at is like, uh,
36:55
Speaker 1: you know, if you have Obama in a concert hall
36:58
Speaker 1: speaking and it sounds like in someone's closet or us, Yeah,
37:02
Speaker 1: it sounds like a can like our earliest episodes. Yeah, exactly,
37:05
Speaker 1: that's pretty you know, pretty strong indicator. It is. So
37:08
Speaker 1: there are things you can do. Um. When BuzzFeed tweeted
37:13
Speaker 1: that Jordan Peel Obama deep fake, they included a list
37:17
Speaker 1: of things to do to spot a deep fake. UM,
37:20
Speaker 1: I wonder how many times you said deep fake in
37:22
Speaker 1: this episode. Don't jump to conclusions. Consider the source. It's
37:30
Speaker 1: a big one. That's what's gonna guide us through here.
37:33
Speaker 1: People like this is Jordan Peel, I can trust that. No.
37:36
Speaker 1: But I mean, like if you go onto a site,
37:38
Speaker 1: I I can spot like a fake news site a
37:40
Speaker 1: mile away, Like you can just tell it's just there's
37:43
Speaker 1: it's off, it's uncanny. It's our sense of uncanny that's
37:46
Speaker 1: gonna guide us through this. Yeah, the it's you can
37:49
Speaker 1: always sell because the screen is black in the texts,
37:51
Speaker 1: florescent green in comic sance. Um. And then another one
37:56
Speaker 1: is check where this thing is and isn't. This is
37:59
Speaker 1: kind of like the opposite tip we always give people
38:02
Speaker 1: where if you see the same thing and basically the
38:04
Speaker 1: same wording throughout the internet, you should question that. If
38:07
Speaker 1: you see a deep fake video and only a couple
38:10
Speaker 1: of places and it's news news, but you don't see
38:13
Speaker 1: it on like ABC or CNN or Fox News or wherever.
38:17
Speaker 1: If you don't see it on like a reputable news site,
38:19
Speaker 1: you should probably question it. Yeah, Donald Trump threatens nuclear war.
38:23
Speaker 1: We have the video from slappy dot com, right, it's
38:26
Speaker 1: probably a good indicator slappy dot com. I'm sure the
38:29
Speaker 1: good people are like, we make we make hamburger button
38:34
Speaker 1: picking up. I should probably check and see what that is.
38:37
Speaker 1: Everyone else is right now, what else? Look closely at
38:40
Speaker 1: their mouth? Yea uh. And then here's a kind of
38:43
Speaker 1: a no brainers, like slow it down, slow the video down,
38:46
Speaker 1: slow your role and like really look at it closely
38:49
Speaker 1: if you see like because that's where you're gonna see
38:51
Speaker 1: like strange lighting changes and stuff. Um, but it's all legal,
38:57
Speaker 1: it is, so we were kind of we're kind of
38:58
Speaker 1: talking about that, like the best way to get a
39:00
Speaker 1: video taking down as to contact the website. UM just
39:04
Speaker 1: be like, bro, come on, this is awful. Um. There
39:07
Speaker 1: are no laws that protect you directly. But a lot
39:11
Speaker 1: of people are saying, well, we've got revenge porn laws
39:13
Speaker 1: that are starting to pop up around the country. It's
39:16
Speaker 1: a very short short trip to from revenge porn to
39:21
Speaker 1: deep fake porn. It's virtually the same thing. It's involuntary pornography. Um,
39:27
Speaker 1: it's even more involuntary because with revenge porn, the person
39:30
Speaker 1: even posed for the picture or whatever initially for whatever
39:33
Speaker 1: context or reason, with no intention to get it out.
39:37
Speaker 1: With deep fake porn, this person never even posed or
39:41
Speaker 1: engaged in this act or anything like that. So it's
39:44
Speaker 1: even even in a way, maybe even worse than revenge porn,
39:48
Speaker 1: which is feels like bitter acid in my mouth to say. Um.
39:52
Speaker 1: So you can make a case though, that these revenge
39:55
Speaker 1: porn statutes that protect people could be extended to this
39:59
Speaker 1: as well, But that's a that's for personal stuff. For
40:04
Speaker 1: like national stuff or a public figure or something like that,
40:08
Speaker 1: especially when it comes to politics. You could make a
40:11
Speaker 1: really strong case that these deep fake videos, even the
40:14
Speaker 1: most misleading, nefarious deep fake video you can imagine, would
40:18
Speaker 1: be protected under the First Amendment. Yeah, I could see
40:21
Speaker 1: a satire defense being mounted in the future. Uh. Like,
40:27
Speaker 1: you know, what's the difference between doing a really good
40:30
Speaker 1: deep fake in doing an animated cartoon like south Park
40:34
Speaker 1: which shows people saying and doing things they wouldn't do either. Uh,
40:38
Speaker 1: it is very slippery and thorny in a very fine line.
40:42
Speaker 1: But even if the person who makes the deep fake
40:44
Speaker 1: says No, I did not mean this is satire. It
40:47
Speaker 1: was meant to be misleading, and I wanted to see
40:49
Speaker 1: what effects it had. Sure they didn't shout fire in
40:53
Speaker 1: a crowded theater, so they could probably still get away
40:56
Speaker 1: with it under the First Amendment. Yeah, it's interesting to
40:59
Speaker 1: see where the just gonna go. Hopefully write down the toilet. Nope,
41:03
Speaker 1: it's just gonna get there. It's gonna get more and
41:06
Speaker 1: more realistic, and we're gonna end up, inadvertently, um, falling
41:10
Speaker 1: into the simulation. That's what's gonna happen. Chuck, prepare for it.
41:15
Speaker 1: That's great, Okay, just try to put a smile on
41:18
Speaker 1: your face regardless. That's smiling. If you want to know more,
41:23
Speaker 1: if you want to know more about deep fakes, Um,
41:27
Speaker 1: it's so hot right now. Just go on the internet
41:30
Speaker 1: and you can read all sorts of news articles about it.
41:33
Speaker 1: Since I said that, it's time for listener mail. I
41:40
Speaker 1: think the first thing that turned me off was the name.
41:43
Speaker 1: Anytime I see something that's like not a real word,
41:46
Speaker 1: but they're like squeezed together to words and it's all
41:48
Speaker 1: lowercase or something or oh gosh, yeah, it's just the
41:53
Speaker 1: worst of the Internet. It's terrible. All right, Hey, guys,
41:57
Speaker 1: just finished the Neanderthal episode and those mentioned that their
42:01
Speaker 1: language could have have some remnants in our modern languages.
42:05
Speaker 1: That was a really good automatically remembered that during How
42:10
Speaker 1: Swearing Works, you guys mentioned that a different part of
42:12
Speaker 1: our brains activate when hearing are using swear words. So maybe,
42:16
Speaker 1: just maybe that small percentage of our name Andrew Falian
42:20
Speaker 1: d n A activates when we stub our toes or
42:23
Speaker 1: hit our shins to unleash our primitive and original language.
42:27
Speaker 1: How about that? I like this person anyways, loved the podcast, guys.
42:30
Speaker 1: Grateful for the knowledge and entertainment. And thank you Jerry,
42:33
Speaker 1: or should we just say thank you Josh Josh T
42:36
Speaker 1: for keeping the quality of these podcasts awesome. We're not
42:40
Speaker 1: thinking Jerry anymore. Just from my overpriced apartment in Redlands, California.
42:46
Speaker 1: That is Falcon. No, that's his last name. Wow, the
42:51
Speaker 1: end is silent though, so it's Falcone. Thanks a lot, Falcon.
42:55
Speaker 1: We appreciate you. Um, that was a great name. Thanks
42:58
Speaker 1: for swooping in with that idea. Mm hm and uh,
43:01
Speaker 1: I'm sorry for everybody for that. If you want to
43:04
Speaker 1: get in touch with us, like foul code. Did You
43:06
Speaker 1: can tweet to us. We're at s y s K podcast,
43:09
Speaker 1: I'm at joshuam Clark. We're on Instagram, We're on Facebook.
43:14
Speaker 1: Where else are we? Chuck Uh, We're on every deep fake.
43:19
Speaker 1: We might be on Giffy who knows gif Uh. You
43:22
Speaker 1: can also send us a good old fashioned email, spank
43:26
Speaker 1: it on the bottom after you wrap it up, of course,
43:28
Speaker 1: and send it off to Stuff podcast at i heart
43:30
Speaker 1: radio dot com. Stuff you Should Know is a production
43:37
Speaker 1: of iHeart Radio's How Stuff Works. For more podcasts for
43:39
Speaker 1: my heart Radio, visit the iHeart Radio app, Apple Podcasts,
43:42
Speaker 1: or wherever you listen to your favorite shows.