“One of the most challenging AI technologies for security teams is a very new class of algorithms called generative adversarial networks (GANs). In a nutshell, GANs can imitate or simulate any distribution of data, including biometric data.
To oversimplify how GANs work, they involve pitting one neural network against a second neural network in a kind of game. One neural net, the generator, tries to simulate a specific kind of data and the other, the discriminator, judges the first one’s attempts against real data — then informs the generator about the quality of its simulated data. As this progresses, both neural networks learn. The generator gets better at simulating data, and the discriminator gets better at judging the quality of that data. The product of this “contest” is a large amount of fake data produced by the generator that can pass as the real thing.
GANs are best known as the foundational technology behind those deep fake videos that convincingly show people doing or saying things they never did or said. Applied to hacking consumer security systems, GANs have been demonstrated — at least, in theory — to be keys that can unlock a range of biometric security controls.”