We toss words around until they lose all meaning, like the name of Jello Biafra’s most famous band. (IYKYK.)
So why are deepfakes deep?
And does the existence of deepfakes necessarily mean that shallowfakes exist?
Why are deepfakes deep?
The University of Virginia Information Security explains how deepfakes are created, which also explains why they’re called that.
“A deepfake is an artificial image or video (a series of images) generated by a special kind of machine learning called “deep” learning (hence the name).”
UVA then launches into a technical explanation.
“Deep learning is a special kind of machine learning that involves “hidden layers.” Typically, deep learning is executed by a special class of algorithm called a neural network….A hidden layer is a series of nodes within the network that performs mathematical transformations to convert input signals to output signals (in the case of deepfakes, to convert real images to really good fake images). The more hidden layers a neural network has, the “deeper” the network is.”
Why are shallowfakes shallow?
So if you don’t use a multi-level neural network to create your fake, then it is by definition shallow. Although you most likely need to use cumbersome manual methods to create it.
- In injection attack detection, no fakery of the image is necessary. You can insert a real image of the person.
- For presentation attack detection (liveness detection, either active or passive), you can dispense with the neural network and just use old fashioned makeup.
Or a mask.
It’s all semantics
In truth, we commonly refer to all face, voice, and finger fakes as “deep” fakes even when they don’t originate in a neural network.
But if someone wants to refer to shallowfakes, it’s OK with me.
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