AI-powered machines are quicker and more accurate at identifying images of fake faces than humans, according to research from biometrics company ID R&D.

People find it much more difficult to pick out images used by fraudsters in spoofing attacks than computers and are nearly 10 times slower, the study found.

On average, it took humans 4.8 seconds per image to determine liveness, whereas computers running on a single CPU took less than 0.5 seconds per image to determine liveness.

Online criminals attempt to imitate real customers during processes like creating a new bank account or logging into an existing account.

Liveness detection instantly validates whether a photo, taken in real time, is of a live person.

The study tested humans and machines by presenting them with the most common spoofing techniques: printed photos, videos, digital images, and 2D or 3D masks.

Computers were more accurate than humans in tests of all five types of images, scoring 0 per cent error rates across all 175,000 images and all types of attack.

Humans had a far lower degree of accuracy for every type of spoofing technique, including misidentifying 30 per cent of photo prints, one of the easiest attack types for fraudsters to execute.

Even when a group of 17 people voted on the images, resulting in a more accurate outcome than an individual person, their majority decisions were never better than the computer’s performance of the same task.

“The results are undeniable,” said Alexey Khitrov, chief executive at ID R&D. “Biometric technology used for identity verification has evolved in recent years to increase speed and accuracy, now significantly outperforming the human eye. Organisations can achieve tremendous efficiencies by using identity verification systems that include a biometric component.

"However, there is still work to be done and we are excited to see biometrics helping to build consumer trust.”

Share Story: