Can we truly believe everything we see?
Throughout human evolution we’ve relied on our sight more than any other sense
to understand our surroundings,
and ultimately inform decisions that ensure our survival.
This is reflected by how central sight is
to our entire concept of knowledge, understanding and truth.
We talk about ‘shedding light’ on a topic.
When we understand someone, we “see what they’re saying”.
But what happens when our sight becomes unreliable?
When seeing is no longer believing?
The huge advances in CGI over recent decades
have normalised the idea of being visually deceived.
The suspension of disbelief in seeing Spiderman swing from skyscrapers,
or spaceships descend from the clouds is all part of the movie-going experience.
Which is fine, because most of these are fantastical showpieces,
not intended to actually fool us.
And even when the visual trickery is more subtle
we either accept it as a storytelling tool, or we don’t even notice.
But while CGI’s heightened believability is certainly impressive,
it could also turn out to be... problematic.
The past few years have seen the rise of Deepfakes:
manipulated videos in which one person’s likeness is replaced with another’s.
The incredibly realistic nature of these videos
means that they can be startlingly believable,
and they’ve already been at the centre of some controversy,
with fabricated videos of politicians and celebrities
ruffling more than a few feathers.
While Deepfakes do utilise
some of the same sophisticated CGI techniques used in Hollywood movies,
it’s important to note that they also differ from them
in at least one crucial way.
While assembling each CGI shot in a blockbuster can take weeks,
- sometimes months -
of painstaking work by a large team, Deepfakes can be created by one person,
sometimes in days, or even hours.
And this is possible because of the process used to create them,
which is known as “Deep Learning”.
Deep Learning is a subset of Machine Learning,
which is a subset of Artificial Intelligence.
Put simply, it works by learning in a similar way to the human brain,
taking in huge amounts of data
and teaching itself to recognise patterns within it.
For example, if you show a Deep Learning algorithm
a few thousand pictures of bananas,
eventually it will learn that anything curved and yellow could be a banana.
The more pictures it has access to, the wider its understanding
of what constitutes a banana, and the more accurately it will recognise them.
Systems like this aren’t perfect, but they’re improving all the time,
and crucially, they’re doing so on their own.
That’s kind of the whole point -
they’re designed to constantly critique their own work,
detecting flaws to improve their techniques.
Even though this technology sounds pretty cutting edge,
it’s already being widely used;
in self-driving cars, google translate, image sorting and
- here’s the important one -
facial recognition.
In the case of Deepfakes,
the algorithm is given as many examples of the same face as possible,
with varying angles, lighting, and expressions,
and it uses this information
to build an ‘understanding’ of the features of that face.
It then combines this information with CG technology,
to insert the new face on top of the existing one,
either re-creating the original expressions and lip movements,
or creating new ones based on user input.
And when you combine these video techniques with similar audio processes,
or recordings made by voice artists,
it becomes possible to insert actors into films they were never in,
or put any words in the mouth of any public figure.
While this technology has made a home for itself
in a surprising range of positive contexts
it’s hard not to be unnerved by the negative implications of its misuse.
A technology which could so easily and effectively mislead
could undermine the basic principles of objective truth that we take for granted,
with potential ramifications far beyond our current understanding.
As such, the last few years have seen
the passing of various articles of Deepfake related legislation,
and a huge amount of discussion about Deepfake detection techniques.
But the ethical and legal considerations of such a new technology
will take a while to be fully defined.
And improved detection techniques come with a brand new headache of their own,
because part of the way Deepfakes work
is to constantly compare their output to a detection system, which means that
better detection techniques will only make Deepfakes harder to detect.
There’s no roadmap for what comes next,
and it’s natural that that’s a little scary. But in a way,
not being able to take for granted that “seeing is believing”
might have the capacity to teach us a valuable lesson,
one which couldn’t be more relevant to our times.
In the age of “Fake News”,
the ability to think critically is taking on a new importance.
And as Deepfakes become more sophisticated
and the opportunity for their misuse grows,
being able to contextualise information
and verify the reliability of where it comes from
are going to become essential life skills
- more than ever before.