Fake videos can now be created using a machine learning technique called a ‘generative adversarial network’ or a GAN.
A graduate student Ian Goodfellow invented GANs in 2014 as a way to algorithmically generate new types of data out of existing data sets.
For instance a GAN can look at thousands of photos of Barack Obama and then produce a new photo that approximates those photos without being an exact copy of any one of them – as if it has come up with an entirely new portrait of the former president not yet taken.
GANs might also be used to generate new audio from existing audio or new text from existing text – it is a multi-use technology.
The use of this machine learning technique was mostly limited to the AI research community until late 2017 – when a Reddit user who went by the moniker ‘Deepfakes’ – a portmanteau of ‘deep learning’ and ‘fake’ – started posting digitally altered pornographic videos.
He was building GANs using TensorFlow – Google’s free open source machine learning software- to superimpose celebrities’ faces on the bodies of women in pornographic movies.
A number of media outlets reported on the porn videos – which became known as ‘deep fakes’.
In response Reddit banned them for violating the site’s content policy against involuntary pornography.
By this stage however the creator of the videos had released FakeApp – an easy-to-use platform for making forged media.
The free software effectively democratized the power of GANs.
Suddenly, anyone with access to the internet and pictures of a person’s face could generate their own deep fake.
When Danielle Citron – a professor of law at the University of Maryland, first became aware of the fake porn movies – she was initially struck by how viscerally they violated these women’s right to privacy.
But once she started thinking about deep fakes she realized that if they spread beyond the trolls on Reddit they could be even more dangerous.
They could be weaponized in ways that weaken the fabric of democratic society itself.