theGIST Issue 12

Science in the Spotlight
Deepfake data in
the post-truth era
Jordan takes a look into the world of
AI-generated images, and investigates
the ugly side of data manipulation.
Artificial intelligence (AI) and machine
learning are amazing tools
and valuable assets to businesses
and laboratories worldwide. As a
microscopist, I've used image analysis
tools, like Trainable Weka [1],
that use machine learning. These
tools make it remarkably easy to
segment images and save time
trawling through datasets. Some
advanced image analysis tools use
neural networks – algorithms that
are designed to work in a similar
way to the brain (at least conceptually
speaking). These algorithms
have the ability and plasticity required
to learn and change behaviours
based on ground truth and
have countless useful applications.
From my own experience, I've realised
that with adequate training,
these tools can separate cellular
structures within images just as
well as a human can (and in some
cases, even better).
What has followed, however, has
been interesting. This approach has
been used to create 'Deepfakes' –
convincing computer generated images
and video clips based on real
faces and voices. Deepfakes are
everywhere, you only need to type
that word into Google or YouTube
and you will be assaulted with a
barrage of fake videos, featuring
celebrities and politicians, all built
from sophisticated neural networks.
Alarmingly, there have even been
several instances of Deepfake porn
using celebrity faces.
What these things all have in
common is that they change data
rather than completely making it up.
Making you look older is easy
enough and with enough images
and audio clips of someone's face
and voice, you can then make them
perform fake speeches. The data is
believable because it's based on
ground truth.
As part of his presentation, Dr
Horváth showed us one of the problems
faced in image processing: the
lack of readily available datasets to
train machine learning tools. Vast
datasets are required to train the algorithms
and this data isn't always
accessible. For example, if you want
to teach a computer how to identify
tumour cells, you need to show it
lots of examples of what a tumour
cell looks like. However, patient data
like this is fiercely protected and difficult
to obtain. We can't possibly
hope to use artificial intelligence to
identify damaged cells if the programme
has never seen one before.
To overcome this, Réka Hollandi
from the Horváth group took the limited
data available and used image
style transfer – a fancy technique –
to produce super realistic images
that are completely separate from
the training data [4]. It's a bit like
me showing you some pictures of
Turns out, machine learning isn't
just good at analysing data – it's
also pretty good at manipulating it.
Assuming you haven't been living
under a rock, you'll remember the
2019 internet craze of FaceApp.
Using neural network-based AI, this
novelty app could make you look
older or younger, and even show
you what you would look like as the
opposite sex – with scarily believable
results in some cases. For a
short time, the app even had features
to make you "hotter", although
this was swiftly removed due to racial
discrimination [2]. The app was
entertaining and I used it extensively
myself, although it quickly lost
its novelty factor.
However, what happens when AI
becomes so smart that it can completely
make up believable images?
I first learned about this while at a
talk given by Dr. Péter Horváth - a
multidisciplinary computational cell
biologist from the Institute of Molecular
Medicine Finland [3].
Dalmatians, then asking you to draw
a Dalmatian from memory. Yes, I've
trained you with some prior knowledge,
but the Dalmatian you draw
will be completely unique. Now imagine
that I did the same with a computer
- and the picture it drew
looked exactly like a real photo of a
never-before-seen Dalmatian. That's
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