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.

StyleGAN is initially used in this approach under a black box optimization method focusing (unsurprisingly) on high dimensional data, since it’s important to find the broadest and most generalized facial features that will satisfy an authentication system.

This process is then repeated iteratively to encompass identities that were not encoded in the initial pass. In varying test conditions, the researchers found that it was possible to obtain authentication for 40-60% with only nine generated images.

The system uses an evolutionary algorithm coupled with a neural predictor that estimates the likelihood of the current ‘candidate’ to generalize better than the p-percentile of candidates generated in previous passes.

https://arxiv.org/pdf/2108.01077.pdf

https://www.unite.ai/master-faces-that-can-bypass-over-40-of-facial-id-authentication-systems/