Don’t believe your eyes: Exploring the positives and negatives of deepfakes

In 2018 the Reddit community r/deepfakes gained international attention
thanks to a piece of investigative
by Samantha Cole, deputy editor at VICE.

Members of the forum had been using a burgeoning technology to
superimpose celebrities’ faces onto pornographic videos. For the general public
– and no doubt the unwitting stars – it was a shock. Most were unaware this
technology existed. Very few believed it was possible to produce such realistic

The videos had been created by a Generative Adversarial Network (GAN), a
machine learning model that uses algorithms to mimic the distribution of data.
In this case, that meant superimposing one human face onto another, complete
with realistic movement. 

Before long the term ‘deepfake’ became part of the common lexicon, both
in porn circles and beyond, and its unceremonious introduction to the world set
the tone for its negative connotations.

But are the origins and misuses of deepfakery causing us to take an
unnecessarily dim view of a potentially useful technology?

Deep trouble

Fast forward to 2019 and the quality of deepfakes is improving at an
exponential rate. Their potential for producing revenge porn, malicious hoaxes,
fraud, misinformation, and blackmail is often the focus of news articles and
think pieces – and not without reason.

Recently, deepfake videos of notable figures like Mark Zuckerberg, US House Speaker Nancy Pelosi, and former
President Barack Obama have all made the
news. And it’s becoming increasingly difficult to tell them apart from the real

After President Trump retweeted a manipulated video of Ms Pelosi,
presumably under the impression it was real, it became clear that the political
implications of this technology could be phenomenal – especially in the age of
‘fake news’ and social media manipulation.  

In fact, the US Intelligence Committee recently issued a warning ahead of the 2020
elections, claiming: “Adversaries and strategic competitors probably will  attempt to use deep fakes or similar
machine-learning technologies […] to influence campaigns directed against  the United States and our allies and

Ten years ago, this would have seemed like sci-fi fantasy. Today, in the
wake of Cambridge Analytica, it is our reality.

Beyond the knee-jerk

The issue surrounding deepfakes is closely aligned to the pertinent
question of our age – what happens when technology advances beyond our
understanding of its implications?

People are concerned, and rightly so. But what’s often overlooked in
favour of knee-jerk reactionism is the potential of deepfakes to do good.

Recently, GANs have been used for educational purposes, like the Dali
Museum’s resurrection of Salvador Dali (it’s less
terrifying than it sounds). And in medical training, where deepfake images are
helping trainee doctors, nurses, and surgeons practice their profession.

GANs can also be used across a range of industries to improve
personalization and immersivity. Retail is an obvious example. Before long it
may be possible for customers to see exactly how they’d look in the products
they’re browsing online.

Likewise, in entertainment, we may not be so far away from becoming the
stars of our own Summer blockbusters. Or at the very least, chief casting

Really, the possibilities are endless. And what happens next will
determine whether we ever get to explore them.    

Striking the right

With Congress taking
steps to potentially criminalize deepfakes
altogether, GAN technology is about to
face a defining moment. But legislation that’s too punitive may be a mistake
when there’s potentially so much to gain – which is why policy-makers and
technologists need to work together to find a solution.

At, we believe in striking a balance; finding a way to protect
vulnerable people from nefarious deepfake use, while still creating an
environment that encourages technical exploration.

Current solutions revolve around technology that can
identify deepfakes
. But as the technology used to create these videos
improves, these countermeasures will have to keep pace.

In response, we’re taking a different approach – working on a concept
that would see all deepfakes registered on a secure, decentralised registry. We
believe this will help to create the right environment to explore the positive
applications of GAN technology, while protecting it – and the wider public –
from exploitation.

With the right legislation and security solutions in place, there’s no reason deepfakes can’t become synonymous with more positive headlines – and we can’t wait to see what the future might bring.

About the author: Jedidiah Francis is the founder of Labs, a Startup Studio dedicated to using Machine Learning to improve the way we live, work and play.

Interested in hearing industry leaders discuss subjects like this and their use cases? Attend the co-located AI & Big Data Expo events with upcoming shows in Silicon Valley, London, and Amsterdam to learn more. Co-located with the IoT Tech ExpoBlockchain Expo, and Cyber Security & Cloud Expo.

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