So, if you have a bit of training in machine learning. There are open source packages available
online where somebody first year could go in to do that. It means that it's kind of
democratized it, in the sense that anybody can access it.
Hi guys, it's Grace here. Thanks for watching today.
That is my voice, but not my face.
This is an example of a deepfake, a word that
combines the terms “deep learning” and “fake.”
It’s an advanced form of artificial intelligence,
which can be used to manipulate faces and voices,
making the process of creating
fake identities and spreading false information much easier.
The technology is threatening to undermine the credibility of almost everything we see online.
From mobile apps that can seamlessly transpose people’s faces into blockbuster movies,
to fake statements made by public figures like Barack Obama and Mark Zuckerberg.
The technology makes things that never happened appear entirely real.
As a consumer, you're going to be watching something, you’re not necessarily going
to know if this is real or not.
Deepfake technology leverages on a deep-learning
system to produce a persuasive counterfeit. This is done through studying photographs
and videos of a target person from multiple angles,
and then mimicking the subject's behavior and speech patterns.
Technology like this has the potential to
affect our elections and jeopardize our personal safety, which is why many companies are already
springing up to battle the cyber threat.
The challenge with this is that it is almost
impossible to regulate. This is something people can actually develop on their own.
To ban it seems to be a bit of a stretch because you want that to generate movies, to do animations.
You know you have a video of a stunt man and you want to change the face to some actor, right?
Malicious use are kind of difficult to discriminate in terms of the actual technology
that is used to generate them. With time we’ll see more clearly what is the threat and then people can
actually devise counter measures.
That’s Ori Sasson. He runs a company called
Blackscore, which has built a Machine Learning platform to identify abnormalities in web
content and on social media.
The video you made for me, that was actually
just from an open source program, right?
So basically, what we did, we used this
open source software, which is available online.
What it is able to do is basically achieve
this outcome of taking a source video, with a new person
and superimpose it on the destination video,
which is for example a video of you.
And all the software needs is a video to manipulate
and a target. In this case, a video of me was uploaded to the software, which then extracted
my many facial expressions. The target was a video of Ori’s colleague,
which he uploaded after the video of me. The software then was able to automatically match
her face to mine when we showed the same movements.
What we did is produce a new video with another
person. The sound is the same, but the face has been replaced. Now you can see that there
are some slight irregularities that the person can notice, however...
But it’s crazy that even the eyebrow movements and the blinking is exactly the same as the
video that I sent you of me.
With deepfake videos becoming more and more
common, how can we tell what is real and what is fake?
Would you be able to tell?
I took to the streets of Singapore to find out.
Scary and risky also. Sometimes if it is properly designed,
then it can be used for some other purposes.
I don't know how to use anything to protect myself,
that's why the best option would be not to go on social media much.
I don't usually frequent Facebook or any social media.
They don't know how to be skeptical enough.
But I think for most people,
we kinda know when it's fake and when it's not?
How can you tell?
We will crosscheck with other sources, of course.
Deepfakes first gained widespread attention in April 2018, when comedian Jordan Peele
created a video that pretended to show former President Barack Obama insulting
President Donald Trump in a speech.
President Trump is total and complete !@#$.
Such videos are becoming increasingly prevalent.
And it’s especially easy for high profile figures to be the targets of these attacks
because there are plenty of publicly available videos of them speaking at various events.
Moving forward, we need to be more vigilant with what we trust from the internet.
Mike Beck is the Global Head of Threat Analysis at a cybersecurity company called Darktrace.
He says we are at the crossroads when it comes to regulation and access to the technology.
We’re just at a very dangerous position where given the access to the
compute power, given developers' access to open AI systems, we’re in a place where actually
deepfakes are genuine things, they’re not farfetched anymore.
I think there is a big gap that governments need to fill, right. Governments tend to be
very, very, slow to this because this is a new emerging technology.
It’s picked up by a much younger generation.
Social media giants are often the targets
for disinformation campaigns, which is likely why platforms like Facebook and Twitter are
beginning to take action on deepfakes.
Shuman Ghosemajumder is the Chief Technology Officer
of Shape Security, a California-based cybersecurity company.
We spoke via Skype to discuss the new policies.
Shuman can you tell me a bit more about Facebook's
and Twitter’s deepfake policies?
Twitter and Facebook have employed slightly
different approaches to dealing with deepfakes.
When they detect that type of manipulated content,
they may place some type of notice on the tweets themselves and then people will
decide how to be able to react to that.
Facebook on the other hand, has specifically
singled out deepfake videos.
But they also have an exception that they’ve made, specifically
for satire and parody videos, and that’s going to be an interesting case in terms of
how they determine whether something is satire or parody.
So how Facebook ends up deciding what’s a parody will make a big difference in terms of what
content ends up getting pulled down or not. So, something that could be uploaded as a
clear parody might stay up. But if that same content is then copied somewhere else, and
the new audience doesn’t realize that it’s a parody, what's the policy there?
Are they going to pull it down?
So far, regulators worldwide have been slow
to clamp down on deepfakes. In the United States, the first federal bill
targeting deepfakes, the Malicious Deep Fake Prohibition Act,
was introduced in December 2018.
And states including California and Texas have enacted laws that make deepfakes
illegal when they’re used to interfere with elections.
Chinese regulators have also announced a ban from 2020 regarding the publishing and distribution
of fake news created using deepfake technology.
Those international conversations are at a
very early stage right now; and it’ll be some time before we have any sort of an agreement
or standards that will allow us to say this is the right approach
that the entire international community can agree on.
It’s a cat and mouse game where you’ve
got AI on one side that is creating fake content, that’s trying to go undetected,
and AI on the other side that is trying to detect that content.
There’s simply too much content that's generated every single day every single hour
on those platforms for humans to be in the middle of a decision-making process.
So just how easy is it to access this technology?
It turns out, easier than you think.
Basically many of these technologies become
much more accessible to normal people without the big budgets and special software.
So that’s where the challenge comes because then it could be abused as we mentioned.
So, you’re saying that if I were just a fourth year uni student studying comp sci,
I could just be creating this in my garage?
Yeah, if you have a bit of training in machine learning using tensorflow,
you could do that and furthermore, actually there are open source
packages available online where somebody who is maybe even first year can go do that.
It means that it is kind of democratized, in the sense that anybody can access it.
Without proper regulation, some fakes may fall into the wrong hands and lead to identity theft.
And there is no black and white answer. Many industries such as film and gaming have
been heavily reliant on “deepfake technology' to make animation look more realistic.
Already, apps with rudimentary face-swapping features are available to consumers.
FaceApp, an app made by a Russia-based developer, allowed users to see
what they might look like when they get older.
And Chinese deepfake app, Zao, let users superimpose their face onto celebrities.
Both went viral in 2019 and sparked numerous privacy concerns.
For many general consumers, if somebody were to create a video of them doing something,
it may not be of much impact, right? I mean it could be a bit creepy,
but it may not have a high impact.
Today, there are a lot of organizations that
do certification exams or other exams by taking a video of the person, he puts up his ID.
For example, if you’re very sophisticated then maybe you can get another person to take
the exam but you create some live modification of the video to put another person’s face
– so I’m taking the exam but actually you’re the one appearing to be taking the exam.
That’s a bit far-fetched in today's technology but it’s not that far-fetched.
And the lower barriers to entry mean you can expect more deepfake apps to be released.
I think there is a big race in the market currently to develop different types of apps.
Developers can be kind of be messing about some of these techniques and start to play
about with images, and you’ll get some developer who is doing really really well and you’ll
see those apps be released on the app stores.
As a consumer you're going to be watching something,
you won't necessarily going to know if this is real or not.
Cybersecurity is not just about protecting your passwords. By some estimates, the current
cybersecurity market is worth more than $150 billion and is
predicted to grow to nearly $250 billion by 2023.
Mike says many companies are turning to biometrics,
facial recognition and voice recognition to add additional levels of security for employee log ins.
But these new tools are a double-edged sword. With deepfakes, these technologies
may present new vulnerabilities for both employees and employers.
As citizens and people who are interacting with Facebook, with YouTube, with Instagram,
we’re all putting videos out there of ourselves – we’re all giving our data away
to a degree, so people who have access to those platforms will be able to see
those images and potentially reuse them.
So, this was about a user who had lost access
to their credential. And an attacker had been able to gain access to the password and also,
they had a voice recognition system as a second factor which is considered really strong but
actually the attacker was able to spoof that voice using an AI system or deepfake,
if you like, and gain access, using that multi-factor, to their corporate systems.
From social media accounts, online e-forms, a huge trove of our personal information is online,
making anyone with a digital presence an easy target.
So how can we prevent ourselves from falling into these traps?
On a corporate side, that becomes easier because we can put in play
more detections and things like that. We can look for anomalies in the systems.
On a consumer scale, that’s so much harder. In terms of protecting, that’s tough.
There’s a genuine problem here at the moment.
If someone really wanted to come after you,
they could put you in the place you’ve never been probably.
You are on camera all the time, there is access to you as a person on the newsreel.
AI platforms can take that image and put them in places you never thought you’d see yourself.
I'm really scared – it sounds like you're saying you're doomed.