'Creative' Facial Verification with Generative Adversarial Networks

A new paper from Stanford University has proposed a nascent method for fooling facial authentication systems in platforms such as dating apps, by using a Generative Adversarial Network (GAN) to create alternative face images that contain the same essential ID information as a real face. The method successfully bypassed facial verification processes on dating applications Tinder and Bumble, in one case even passing off a gender-swapped (male) face as authentic to the source (female) identity. Various generated identities which feature the specific encoding of the paper's author (featured in first image above). According to the author, the work represents the first attempt to bypass facial verification with the use of generated images that have been imbued with specific identity traits, but which attempt to represent an alternate or substantially altered identity. The technique was tested on a custom local face verification system, and then performed well in black box tests against two dating applications that perform facial verification on user-uploaded images.