'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. The Israeli system workflow, which uses the StyleGAN generator to iteratively seek out'master faces'. The new method improves on a similar recent paper from the University of Siena, which requires a privileged level of access to the machine learning framework.