The Reality-Distorting Tools Of The Future
They say that seeing is believing—but soon, that old proverb could be out of date.
For decades, Adobe Photoshop and Hollywood-grade CGI have allowed artists and filmmakers to manipulate images for entertainment. But a slew of new artificial intelligence-assisted tools could allow the average person to effectively make a video of any person do or say whatever they want.
In December 2017, technology website Motherboard reported on r/deepfakes, a Reddit community that used open-source face-swapping applications to digitally insert classmates, friends, and even celebrities like Wonder Woman actor Gal Godot and pop star Taylor Swift, into pornographic films. This practice of what Reddit calls “involuntary pornography” has since been banned from Reddit, Twitter, adult website PornHub, and several other web communities, but other, more “safe for work” versions of the same technology—including a video of actor Nicolas Cage face-swapped into several films, or an actor’s portrayal of Adolf Hitler superimposed over a speech by Argentine president Mauricio Macri—have raised questions about how technology can misrepresent intentions.
Beyond face-swapping, other tools could soon allow formerly “difficult to fake” media to be manipulated. Chinese company iFlyTek recently produced audio of president Donald Trump speaking Mandarin. In 2016, Adobe introduced the as-of-yet unreleased Project Voco, a tool some have called the “Photoshop of speech.” Project Voco would easily allow someone to alter a digital recording to make it sound like someone said words they had never uttered. And using generative adversarial networks, essentially a “cat and mouse” game between two competing AIs, Nvidia was able to develop a grid of fake celebrities faces.
All of this could be leading to what technologist Aviv Ovadya refers to as an “information apocalypse.” In a recent Buzzfeed profile, he warns that AI-assisted technology, used maliciously, could spread propaganda by appearing to manipulate reality. He, and other researchers, are trying to raise the alarm.
Ovadya chats with Ira about the potential upcoming information crisis and what we can do to fight it. Motherboard reporter Samantha Cole joins to talk about the “deepfakes” phenomenon, alongside digital forensic scientist Siwei Lyu, who talks about how we can spot AI-assisted fakes.
On how “deep fakes” and similar technology work.
Samantha Cole: It’s done using a machine learning algorithm. It takes a data set of lots of pictures of one person’s face, so hundreds of pictures of, say, Carrie Fisher, and then a video to put that onto. It runs these two together in the algorithm and what comes out after hours or days is what looks like that person in that video.
Siwei Lyu: What has been changed recently, exactly as some have mentioned, is are these new artificial intelligence-enabled algorithms that can take a lot of data and bypass a lot of this manual process, and a need for technical facilities and make this technology accessible to many many of the users who may not afford these kinds of technical setups.
On the implications of wide adoption of faked video.
Samantha Cole: I think that for a long time video has been our kind of gold standard of truth—maybe not legally, but definitely in our minds. You see something you say, “Oh my gosh, that happened!” You see it for five seconds; you hit share and retweet and it gets a million shares on Facebook and then that is what happened. And that’s the scariest part of this—it doesn’t have to be that believable to actually be seen as true.
On how AI-assisted algorithms could make diplomacy difficult.
Samantha Cole: Things are already pretty bad with fake news. We saw this had become the big news item in 2016 about the election. People were slicing up videos to make it look like things happened that didn’t. This is just the next level in that. How we view things as true a lot of the time is if you see a video and you see that person in the video, you can say that happened. Now, that’s not the case.
Aviv Ovadya: It’s not just video—it’s also audio. That can have a really big impact on, as we’ve talked about, diplomacy…Let’s say there’s a hot mic of Trump ordering a missile attack on North Korea. Didn’t actually happen. Doesn’t mean North Korea won’t launch a missile attack back.
On how to detect it.
Siwei Lyu: One clue we’re are using, working with my students at the University of Albany and also a professor from Dartmouth College, where he’s talking [about] this idea of using some physiological signals to detect fake videos generated by an algorithm, like a deep fake. So basically our algorithm identified tiny fluctuations in the skin colors due to changes from the blood flow. As you know, every person has a heartbeat, so this heartbeat, when they pump [blood] flow in and out of the skin, there was actually a very tiny change of the skin color. Now with a technique that is developed by researchers from MIT, we actually can enhance the video after the fact and then use that as a signal to differentiate…if you have a video is actually from a real person, you would see this signal very prominently. On the other hand, on a generated fake video, the signal of this kind of physiological phenomena will be much weaker.
On the challenges of detecting fakes.
Aviv Ovadya: I would say that one of the challenges with all of these approaches is that once you have a system that can detect a fake, then you can train your system that creates fakes to counter that system. And so as long as there’s access to that system for detection, you can just get better and better at sort of getting past it. So I don’t see that as sort of a super long term solution. I mean, it’s a cat and mouse game.
On what will happen when there’s a blizzard of fake information.
Aviv Ovadya: If we can’t believe anything, that’s sort of what a failed state looks like. That’s what happens in conflict regions. And it’s really hard to run a democracy like that…I mean, it’s not going to be quite that much of falling off a cliff that quickly. But I would say that our information system is going to fairly rapidly get worse as this stuff develops and we need to really put stuff in place to help prevent that from accelerating as fast as it might otherwise.
These interview excerpts has been edited and condensed for clarity and length.
Samantha Cole is a reporter at Motherboard.
Siwei Lyu is an associate professor in the department of computer science and the director of the Computer Vision and Machine Learning Lab of the College of Engineering and Applied Sciences at the University at Albany, State University of New York.
Aviv Ovadya is the Chief Technologist at the Center for Social Media Responsibility of the University of Michigan. He’s based in San Francisco, California.
IRA FLATOW: This is Science Friday. I’m Ira Flatow.
Last December, Motherboard assistant editor Samantha Cole found something strange on Reddit. Someone, a user named deepfakes was making realistic looking pornographic videos by swapping the faces of well-known actresses into actual not safe for work films– Wonder Woman’s Gal Gadot, Taylor Swift, others seemingly all becoming porn stars by having their faces swapped out.
Then someone made an app that anyone could use to do the same thing. And the results have been more face swap porn, endless iterations of Nick Cage in movies he’s never starred in, and other gags. But the app is also being used on real people’s pictures and not just actresses. But you, your spouse, your friend, potentially. It’s all a new kind of revenge porn, if you like.
Reddit has since banned the pornographic stuff, saying it violates the rights of the women whose faces were used. But the technology is still out there. You can’t put the genie back into the bottle. And is this one more step to making reality as we know fuzzier than ever? We’re going to get to that issue in a few minutes. But first, Samantha Cole is here with me to talk more about the deepfake story. Welcome to Science Friday.
SAMANTHA COLE: Hi. Great to be here.
IRA FLATOW: How did you stumble across this phenomenon?
SAMANTHA COLE: We were made aware of it on Twitter. Someone spotted this person on Reddit going along about his hobby of putting celebrities’ faces onto porn performers’ bodies using AI.
IRA FLATOW: No, I was wondering how this works because I saw one example that looks like a cheaper version of the way the 1970s Carrie Fisher was brought back to life at the end of Rogue One. How does this work?
SAMANTHA COLE: Well, basically, like you said, it’s done using a machine learning algorithm. It takes a data set of lots of pictures of one person’s face– so, hundreds of pictures of, say, Carrie Fisher– and then a video to put that onto. It runs these two together in the algorithm. And what comes out after hours or days is what looks like that person in that video.
IRA FLATOW: I know it’s not that easy, because we here at Science Friday tried to make a fake video video using my face at the office and paste me onto Humphrey Bogart in Casablanca, and it was not pretty.
It’s harder than we expected. So how accessible is this to the average person?
SAMANTHA COLE: Right. You’re right. It’s not something that you can just plug into your computer or your iPhone. It’s not an app in that way. It comes with tutorials and things like that so that you can follow along and do it yourself. We say that anyone can do it, but it does take a lot of patience, some curiosity, and a little bit of knowledge about AI to begin with, a huge data set of the person’s face, some decent computing hardware, a pretty powerful GPU, things like that. So it’s accessible and it’s democratized, but I’m not going to say it’s easy.
IRA FLATOW: But I know, as I said before, platforms like Reddit are shutting down the pornographic fakes. But that doesn’t really stop people from creating fake porn or distributing it in other ways, right? Not on Reddit, some other places.
SAMANTHA COLE: No, you’re completely correct. It’s not going away just because these platforms shut them down. They’re just being driven to more scattered places on the web.
So, Discord, which is a chat platform, has done some work on banning them. A few of the image hosting sites. Pornhub even said that they won’t allow it. So they’ve denounced it, and that’s a great step, but it’s not going to disappear from the internet.
IRA FLATOW: One of the face swap videos out there puts the face of Adolf Hitler onto Argentine leader Mauricio Macri, which begs the question of what this kind of technology could do to diplomacy–
SAMANTHA COLE: Sure.
IRA FLATOW: –down the road.
SAMANTHA COLE: Things are already pretty bad with fake news. We saw this become the big news item in 2016 with the election. People were splicing up videos to make it look like things happened that didn’t. This is just the next level in that. How we view things as true a lot of the times is if you see a video, and you see that person in the video, and you can say, that happened. And now maybe that’s not the case.
IRA FLATOW: It’s getting fuzzy. Reality is getting fuzzier all the time.
So I want to expand the conversation and bring in two more guests now to help us take this conversation to the bigger picture, what this kind of technology means for reality and fake news. Aviv Ovadya is chief technologist for social area of responsibility at the University of Michigan’s school of information. Welcome, Aviv.
AVIV OVADYA: Hi.
IRA FLATOW: Hi. And Siwei Lyu is an associate professor of engineering and applied sciences at the State University of New York in Albany. And he works on detecting digital forgeries. Welcome, Dr. Lyu.
SIWEI LYU: Hi, good to be here, thank you.
IRA FLATOW: You’re welcome. Aviv, you were recently profiled in an extensive Buzzfeed piece talking about face swapping AI, but also other kinds of video forgery. What else is out there?
AVIV OVADYA: Yes. It’s not just video, it’s also audio. And that can have a really big impact on, as we talked about, diplomacy, where, for example, let’s say there’s a hot mic of Trump ordering a missile attack on North Korea. Didn’t actually happen. Doesn’t mean North Korea won’t launch a missile attack back.
IRA FLATOW: And in fact, we actually have an example of this how we’re seeing audio forgeries. Speaking of the president, here’s one that recently came to my attention.
– It’s a great way to build a better world with artificial intelligence. AI first.
IRA FLATOW: I didn’t know the president could speak Mandarin. That was from a Chinese company called iFlyTek. They also created audio of President Obama speaking Mandarin. And Adobe previewed a Photoshop for voice two years ago. It’s not hard to see why you’re warning about worsening fake news, Aviv. What do you see happening? Is this something we should really worry about or is it just for fun now?
AVIV OVADYA: It’s something that we should really worry about. It affects the foundations of our democracy and a lot of our civil institutions.
IRA FLATOW: Well, let me ask you, Dr. Lyu– what is the technology powering all of these fakes? Is it just old image manipulation getting more advanced or is there something new here?
SIWEI LYU: Actually, I would say research and techniques for manipulating digital images and videos existed many years. And this actually goes back to the film industry– if you have seen Forrest Gump or computer graphics generating whole feature movies.
However, previously this has the need to use special hardware to software systems and a tactical artistic training and [INAUDIBLE]. What has been changed recently, exactly as Samantha mentioned, is this new artificial-intelligence-enabled algorithms that can take a lot of data and bypass a lot of this manual process and a need for technical facilities and make this technology accessible to many, many of the users who may not afford this kind of technical setups.
IRA FLATOW: Are we seeing other kinds of forgeries– fake signatures, other kinds of fraud out there, Dr. Lyu?
SIWEI LYU: Well, I think actually the fake images exist– we’re paying a lot of attention to these high profile cases, but actually this forgery, media forgeries, exist in a much wider range.
Actually, I myself have experience with cases in insurance where you take a photograph of a car accident and you digitally doctor it, makes it either severe or less severe depending on what do you need. Or, also swapping scientific papers. There are a lot of papers, so people realize that the figures in the paper were actually doctored. And again, other than social and political, economical impacts, there is also this impact of eroding our trust for the visual media.
IRA FLATOW: Aviv, I know you must see a lot of cases of forgeries, fake stuff out there. What’s the worst one you can share with us?
AVIV OVADYA: There hasn’t been something that’s actually deeply affected diplomacy using video or [INAUDIBLE] manipulation yet, so I would say that the worst is yet to come. But I think what’s terrifying is that even stuff that’s really bad– even without using any of this technology, without having it be convincing at all– just a tweet about a fake news story can lead to the Pakistani– I can’t remember– took a position they had in government. But they responded rattling sabers around nuclear weapons. And that was about a year ago, if I recall correctly. So if that alone can have this impact, then what happens when you have something that’s extremely believable?
IRA FLATOW: Why is fake news so much worse, Samantha and Aviv, if video is involved? Does video make it seem like it’s got to be real, Samantha? If I see it, then seeing is believing.
SAMANTHA COLE: Yeah. I think that for a long time video has been our gold standard of truth, and not maybe not legally, but definitely in our minds. You see something, you say, oh my gosh, that happened. You see it for five seconds, you hit share and retweet, and it gets a million shares on Facebook. And then that is what happened. And that’s the scariest part of this, is it doesn’t have to be that believable to actually be seen as true. So the video aspect is definitely concerning.
IRA FLATOW: Dr. Lyu, let’s talk about how we can fight forgeries. I know you told my producer about one method that you’re still researching. This sounded amazing. It involves heartbeats?
SIWEI LYU: Yes. We are actually tackling this problem of fake videos generated the with AI synthesized faces.
So one clue we’re using– actually working with my student at University at Albany and also Professor Hany Farid from Dartmouth College– we’re actually exploring this idea of using some physiological signals to detect fake videos generated by algorithms like deepfake. So basically, our algorithms identify tiny fluctuations in the skin colors due to changes from the blood flow.
As you know, every person has heartbeats. So this heartbeat, when the pump flow in out of the skin, there was actually a very tiny change in the seen colors. Now with a technique that is developed by researchers from MIT, we actually can enhance the video, enhance that effect, and then use that as a signal to differentiate between a video generated from a real person or a video actually generated from an organism. So if we assume that the video is actually from a real person, we should see the signal very prominently. On the other hand, from a generated fake video, the signal of this kind of physiological phenomenon will be much weaker.
So we have some preliminary results. Seem to be promising. But we are still exploring this idea.
IRA FLATOW: And, Samantha, you noted on your website already enlisting AI to spot AI-generated videos. Does it seem like algorithms will be enough to fight this?
SAMANTHA COLE: It’s definitely an option. It’s something that’s a lot of really smart people in the AI space have been working on for a really long time. This didn’t come out of nowhere. It’s just that more people have their hands on it now.
So there are people working on things like the heartbeat detection, aspects like that. Video fingerprinting is definitely a big one. It’s tricky because AI algorithms are still written by humans.
IRA FLATOW: Right.
SAMANTHA COLE: So, yeah, it’s interesting to watch these chase each other around to tackle the problems that we have now.
IRA FLATOW: Aviv, what are you looking at doing? Anything about fighting back?
AVIV OVADYA: Well, I would say that one of the challenges with all of these approaches is that once you have a system that can detect a fake, then you can train your system that creates fakes to counter that system. And so as long as there is access to that system for detection, you can just get better and better at getting past it. So I don’t see that as a super long-term solution. It’s a cat and mouse game. I think that we really need a defense in-depth approach here, where you’re tackling this across different aspects of the way this is impacting society in addition to these technical solutions.
And even if you can detect whether or not something has been manipulated, that still needs to be shown in some way when it’s being represented– on Facebook, on YouTube, wherever. And so this is a not just can you detect it, but also how does this actually affect the ecosystem around which we share information.
IRA FLATOW: I’m Ira Flatow. This is Science Friday from PRI, Public Radio International. Talking about fake video voices, creating fake identities, fake conversations.
Let me ask all my panel. You’ve got face swapping, and lip syncing, voice imitation, weather alteration. When you consider the potential to alter reality as we know it, really makes me wonder why are we researching these technologies in the first place. Are there any good reasons to have them? Are there beneficial uses?
AVIV OVADYA: Yeah.
SAMANTHA COLE: Sure, there are–
AVIV OVADYA: There are, of course.
SAMANTHA COLE: –good things that come out of this.
IRA FLATOW: Go ahead. Why don’t you go first–
SAMANTHA COLE: Sorry.
IRA FLATOW: –Sam.
SAMANTHA COLE: There are definitely good things to come out of this. This is technology that’s been in use in Hollywood for years. We saw the same thing happen in Rogue One with Princess Leia’s face. They put Carrie Fisher’s face basically using the same technology or a really similar technology. So there are uses in security, and someone wants to obscure their identity in a video, and things like that.
I don’t think that it’s a bad thing that we’re looking at this stuff and researching it. I think putting it all out there in the light is always good.
IRA FLATOW: Anyone else have a suggestion of what good stuff we could come in.
SIWEI LYU: Yeah. I think at the very beginning all of this research are from scientific curiosity, so what kind of system we can build that can simulate human behaviors and can help us understand the world better. And I think that’s the ground goal of artificial intelligence at the very beginning.
I think all we’re seeing– this– is actually a byproduct of the weapon of the technology. So it is not really right or wrong for this technology. This is, I think, with a very innocent purpose on that level. It is just how we actually use this. So it’s being abused in this case.
But there are many other ways we can use AI to help us. For instance, predicting better, running tons and tons of data how we can actually relieve this intensive menu processing or intensive computation by just directly using data and training this algorithm to do this automatically.
I think there will be a lot of more positive impacts of the AI technology development. But this byproduct is something we should also take very seriously.
IRA FLATOW: Our number 844-724-8255. You can call us and also get us on [? SciTalk. ?] About a minute to go before the break. Aviv, did you want to jump in?
AVIV OVADYA: Yeah. I think that it’s important that as we develop the sort of technology that we really ensure that we have organizations in place that can invest in understanding what these impacts might be on society, on elections, on democracy, on our courts. And I think that that’s something that hasn’t happened, and that we’re going to start seeing the impacts of us not investing in that as we do invest in these new technologies. And I think that that’s something to really keep in mind– how to ensure that 10% of the budget going into AI is also going into mitigating some of the negative impacts in addition to thinking about the positive.
IRA FLATOW: OK. We’re going to come back and talk more about this and how it might affect upcoming future events. Our number, 855-724-8255. Stay with us. We’ll be right back after the break.
This is Science Friday. I’m Ira Flatow. We’re talking this hour about the future of our information ecosystem, trust in the news in an age when AI might soon be able to forge seamless videos and audio of world leaders, news events, and more. It’s interesting they we’re talking about this today when
Counsel Mueller indicts 13 Russians for election meddling and talks about all the kinds of different tricks and stuff that they were using. Dr. Lyu, does that have any relevance to upcoming elections, all this fraud and stuff that’s going on with video and audio?
SIWEI LYU: I can’t predict the future. But I think if this technology keeps advancing that we will reach a level where it will become very difficult to tell if a video is a real one or is a fake one just by a casual glance. So I think this is potentially something that is very serious.
IRA FLATOW: Aviv, do you agree?
AVIV OVADYA: Yeah. And I think that that actually helps the other side win. If we can’t believe anything, that’s what a failed state looks like. That’s what happens in conflict regions. And it’s really hard to run a democracy like that.
IRA FLATOW: And so how much time do you give us then to get to that failed democracy?
AVIV OVADYA: It’s not going to be quite that much of a falling off a cliff that quickly. But I would say that our information ecosystem is going to fairly rapidly get worse as this stuff develops. And we need to really put stuff in place to help prevent that from accelerating as fast as it might otherwise.
IRA FLATOW: Siwei, what can we do in the meantime until we get there?
AVIV OVADYA: Well, I think investing in the forgery stuff is something that’s really important to do for now, forgery detection.
SIWEI LYU: Right. I’m actually working in a program that is sponsored by DARPA known as a media forensics. So this is a four year large-scale program that aims to assemble teams across academia, industry, and government– as well as international partners– to provide technical solutions to this problem.
So the holy grail after the completion of this program, we hope, is we’re able to provide a platform, an interface, or something you can think about as a crowd-checking platform where every image or video, the user can actually upload that media to a place and then some kind of objective decision about the authenticity of those medias will be returned back.
So as a research community we’re working very hard toward that goal. So this gives a little bit of hope for this problem. I think we’re working hard. We’re trying to fight back this trend. And as Aviv just mentioned, this is a cat and mouse game. So we keep growing, both sides of the war.
IRA FLATOW: Samantha Cole, you found this– you say you stumbled upon this on Reddit.
SAMANTHA COLE: Yeah, pretty much.
IRA FLATOW: So I always have the 1% or the 10% theory of the iceberg– you only see a tiny tip of something sticking up. There must be so much more of this going on.
SAMANTHA COLE: Sure. It was just one person that we saw at the time. There could have been more. He was the first that we saw doing it. And then from that it grew into a community that was nearly 100,000 subscribers strong on one subreddit. So he was definitely the tip of the iceberg that was to come.
IRA FLATOW: Let me see if I can get a call in here before we run out of time. Let’s go to John in Maryland. Hi, John.
JOHN: Hey, Ira. How you doing there?
IRA FLATOW: Hi there. Go ahead.
JOHN: Well, listen. I listen to the show because I love science. And I love this show. But I’ve got to tell you, when the guest mentioned earlier one of the reasons to do it is pure scientific curiosity, my reaction is this– to me, it’s the adult version of playing with matches and gasoline as a child. I really think this opens a door to destroying public trust in information during electoral process, during emergencies, and God only knows what.
IRA FLATOW: Yeah, that’s a good point. Let me let me ask Aviv what do you think about that? Is everything just now going to be untrustworthy?
AVIV OVADYA: I definitely think that there is an element of truth to that. And that’s why I say that we need countervailing investment– when we’re investing in new technologies around video production and creation, then also invest in ways of mitigating those harms.
I think it’s also really important that the companies that are actually the distribution platforms for this really invest in ensuring that they’re part of this ecosystem in a healthy way and that they’re not necessarily rewarding the things that are fake, which is the default that might have happened in the past just because of the way that they reward things that get a lot of attention and that seem very sensationalist.
IRA FLATOW: The new technologies of blockchains are supposed to be very secure. Is that something, Siwei, that might be useful?
SIWEI LYU: Potentially yes. One way actually to protect authenticity of digital media is actually actively [INAUDIBLE] some I would say fingerprint or hidden character to those media so that anybody making any change, there is a potential way to recover that. So this new technology of blockchain because it’s secure, that provides some hope to protect the authenticity of this media.
But it doesn’t solve the problem of if something is completely generated by AI algorithm. So I think it solves one part of the problem, which is protecting the original media. But probably for this new generated synthetic media it’s not as effective.
IRA FLATOW: Do you think that that real money is going to have to be lost on this? That’s when business gets really serious about doing something about something like this. For example, someone is tweeting– from Ryan– can you talk about copyright law on this technology? Once people start losing money that way. Sam, what do you think?
SAMANTHA COLE: Well, that’s true that celebrities and people who are in the public eye have a little more control over their images. So if they made a lot of noise about this, maybe that would be something that some of these platforms would take some notice on. They could sue for misappropriation of their images, things like that. But laws that protect the average person are not quite equipped to handle this. It doesn’t mean that we need new laws to cover it, but we need to improve the laws that we already have.
IRA FLATOW: But no one is getting off the internet. That we know.
SAMANTHA COLE: Not that we know of.
IRA FLATOW: That’s here to stay.
SAMANTHA COLE: Except for me.
IRA FLATOW: Are you getting off the internet?
SAMANTHA COLE: No.
IRA FLATOW: No, OK.
SAMANTHA COLE: Unfortunately.
IRA FLATOW: I want to thank my guests Aviv Ovadya, chief technologist for social area of responsibility, University of Michigan’s school of information; Samantha Cole, assistant editor at Motherboard; and Siwei Lyu, associate professor of engineering and applied sciences at State University of New York in Albany. Thank you all for taking time to be with us today.
SAMANTHA COLE: Thank you.
SIWEI LYU: Thank you very much.