IPhone X to use 'black box' anti-spoof Face ID tech

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 Apple's Facial ID system relies on two types of neutral networks. According to a guide released by the firm, One of which has been specifically trained to resist spoofing attempts.
But a consequence of the design is that it behaves like a "black box".
So, while Apple says Face ID should be able to distinguish between a real person's face and someone else wearing a mask that matches the geometry of their features, it will sometimes be impossible to determine what clues were picked up on.
Face ID is far from being the first facial recognition system to be built into a mobile device.

But previous technologies have been plagued by complaints they are relatively easy to fool by with photos, video clips or 3D models shown to the sensor.

This has made them unsuitable for payment authentication or other security-sensitive circumstances.

In publishing its Face ID documentation more than a month ahead of the iPhone X going on sale, Apple is hoping to head off such concerns - particularly since the handset lacks the Touch ID fingerprint sensor found on its other iOS phones and tablets.

Its website details how the process works:

    * two sets of readings are taken by the sensors - a "depth map" of the face created by shining more than 30,000 invisible infrared dots on it, and a sequence of 2D infrared images
    * these readings are taken in a randomised pattern that is specific to each device, making it difficult for an attacker to recreate
    * the data is then converted into an encrypted mathematical formulation
    * this is then compared to similarly encoded representations of the owner's face stored within a "secure" part of the processor.

The final "matching" part of the procedure relies on one neural network that Apple says was trained using more than a billion 2D infrared and dot-based images.
Simultaneously, it explains "an additional neural network that's trained to spot and resist spoofing" comes into play.

Apple has designed the computer chip involved to make it difficult for third parties to monitor what is going on, but even if they could they would have little chance of making sense of the calculations.

"The developers of these kinds of systems have some level of insight into what is happening but can't really create a narrative answer for why, in a specific case, a specific action is selected," explained Rob Wortham, an artificial intelligence researcher at the University of Bath.

"With neural networks there's nothing in there to hang on to - even if you can inspect what's going on inside the black box, you are none the wiser after doing so.

"There's no machinery to enable you to trace what decisions led to the outputs."

Apple has said it carried out many controlled tests involving three-dimensional masks created by Hollywood special effects professionals, among other tasks, to train its neural network into detecting spoofs.

However, it does not claim it is perfect, and intends to continue lab-based trials to further train the neural network and offer updates to users over time.