"Master Faces" that Can Bypass Over 40% of Facial ID Authentication Systems

upstart writes:

Master Faces' That Can Bypass Over 40% Of Facial ID Authentication Systems:

Researchers from Israel have developed a neural network capable of generating master' faces - facial images that are each capable of impersonating multiple IDs. The work suggests that it's possible to generate such master keys' for more than 40% of the population using only 9 faces synthesized by the StyleGAN Generative Adversarial Network (GAN), via three leading face recognition systems.

The paper is a collaboration between the Blavatnik School of Computer Science and the school of Electrical Engineering, both at Tel Aviv.

Testing the system, the researchers found that a single generated face could unlock 20% of all identities in the University of Massachusetts' Labeled Faces in the Wild (LFW) open source database, a common repository used for development and testing of facial ID systems, and the benchmark database for the Israeli system.

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