Name: How Machine Learning Drives the Deceptive World of Deepfakes
Uploaded: 2020-03-28T10:24:06.000Z
Duration: 7 min 32 s
Description: Thousands of YouTube videos with English-Chinese subtitles! Now you can learn to understand native speakers, expand your vocabulary, and improve your pronunciation...

This is all a conspiracy, don't you know that, it's a conspiracy. Yes, yes, yes!

Good evening, my fellow Americans. Fate has ordained that the men who went to the moon to explore in

Adverbs of degree

peace will stay on the moon to rest in peace. That President Nixon video you just watched is

a deep fake. It was created by a team at MIT as an educational tool to highlight how

manipulated videos can spread misinformation - and even rewrite history. Deepfakes have

become a new form of altering reality, and they’re spreading fast. The good ones can

chip away at our ability to discern fact from fiction, testing whether seeing is really

believing. Some have playful intentions, while others can cause serious harm. People have

had high profile examples that they put out that have been very good, and I think that

moved the discussion forward both in terms of, wow, this is what's possible with this

given enough time and resources, and can we actually tell at some point in time, whether

things are real or not? A deep fake doesn't have to be a complete picture of something.

It can be a small part that's just enough to really change the message of the medium.

See I would never say these things, at least not in the public address. But someone else

A deep fake is a video or an audio clip that's been altered

to change the content using deep learning models. The deep part of the deep fake that

you might be accustomed to seeing often relies on a specific machine learning tool. A GAN

is a generative adversarial network and it's a kind of machine learning technique. So in

the case of deep fake generation, you have one system that's trying to create a face,

for example. And then you have an adversary that is designed to detect deep fakes. And

you use these two together to help this first one become very successful at generating faces

that are very hard to detect by using another machine learning technique. And they just

go back and forth. And the better the adversary, the better the producer will be.

One of the reasons why GANs have become a go-to tool for deep fake creators is because of the data

revolution that we’re living in. Deep learning has been around a long time, neural networks

were around in the '90s and they disappeared. And what happened was the internet. The internet

is providing enormous amounts of data for people to be able to train these things with

armies of people giving annotations. That allowed these neural networks that really

were starved for data in the '90s, to come to their full potential. While this deep learning

technology improves everyday, it’s still not perfect. If you try to generate the entire

thing, it looks like a video game. Much worse than a video game in many ways. And so people

have focused on just changing very specific things like a very small part of a face to

make it kind of resemble a celebrity in a still image, or being able to do that and

allow it to go for a few frames in a video.

Deep fakes first started to pop up in 2017,

after a reddit user posted videos showing famous actresses in porn. Today, these videos

still predominantly target women, but have widened the net to include politicians saying

and doing things that haven't happened. It's a future danger. And a lot of the groups that

we work with are really focused on future dangers and potential dangers and being abreast

of that. One of these interested groups has been DARPA. They sent out a call to researchers

about a program called Media Forensics, also known as MediFor. It's a DARPA project that's

geared towards the analysis of media. And originally it started off as very much focused

on still imagery, and detecting, did someone insert something into this image? Did someone

remove something? It was before deep fakes became prominent. The project focus changed

Aaron and his team have been working across disciplines to create a multi-pronged approach

for detecting deep fakes. The system they’ve developed is called SAVI. So our group focused

on speech. And in the context of this SAVI program, we worked with people in the artificial

intelligence center who are doing vision. And put our technologies together to collaborate

on coming up with a set of tools that can detect things like, here's the face. Here's

the identity of the face. It's the same person that was earlier in the video. The lips are

moving, okay. And then we use our speech technology and say, "Can we verify that this piece of

audio and this piece of audio came from the same speaker or a different speaker?" And

then put those together as a tool that would say, "If you see a face and you see the lips

moving, the voice should be the same or you wanna flag something." However, there is always

a worry that making these detection systems more available could unintentionally provide

deep fake creators with workarounds. If released, the methods meant to catch the altered media,

could potentially drive the next generation of deep fakes. As a result, these detection

systems have to evolve. In its newest iteration, Aaron gave us a run through of how various

aspects of the system work, without giving too much away. This is an explicit lip sync

detection. What we're doing here is we're learning from audio and visual

tracks what the lip movement should be given some speech and vice versa. And we're detecting

when that deviates from what you would expect to see and hear. While some techniques can

work well on their own, most fair better when combined into a larger detection system. So

in this video you'll see Barack Obama giving a speech about Tom Vilsack, one of his departing

cabinet members. And we're running this live through our system here, which is processing

basically to identify two kinds of information. The top one where it says natural is a model that's

detecting is this natural or some type of synthesized or generated speech, essentially

a deep fake. In the bottom, is detecting identity based on voice, so we have a model

of Barack Obama so it's saying this continues to verify as Obama and this will continue

like this until now we get Jordan Peele imitating Barack Obama. We're entering an era in which

our enemies can make it look like anyone is saying anything at any point in time. And

that whole section here was Jordan Peele. He’s natural, but he’s not Obama. I would

say for detection of synthesis or voice conversion, we're in the sub 5% error rate for what I

would call laboratory conditions. And probably in the real world, it would be higher than

that. That's why having these multi-pronged things is really important. However, technology

is only part of the equation. How we as a society respond to these altered pieces of

content is as important. The media tends to focus on the technological aspects of things

rather than the social. The problem is less the deep fakes and more the people who are

very willing to believe something that is probably not well done because it confirms

something that they already believe. Reality becomes an opinion rather than fact. And it

gives you license to misbelieve reality. It's really hard to predict what will happen. You

don't know if this is going to be something that five years from now people actually nail

down or if it's 40 years from now. It's one of those things that is still sort of exciting,

interesting and new and you don't know what the limitations are yet.