Master Face Attacks on Face Recognition Systems

Face authentication is now widely used, especially on mobile devices, rather than authentication using a personal identification number or an unlock pattern, due to its convenience. It has thus become a tempting target for attackers using a presentation attack. Traditional presentation attacks use facial images or videos of the victim. Previous work has proven the existence of master faces, i.e., faces that match multiple enrolled templates in face recognition systems, and their existence extends the ability of presentation attacks. In this paper, we report an extensive study on latent variable evolution (LVE), a method commonly used to generate master faces. An LVE algorithm was run under various scenarios and with more than one database and/or face recognition system to identify the properties of master faces and to clarify under which conditions strong master faces can be generated. On the basis of analysis, we hypothesize that master faces originate in dense areas in the embedding spaces of face recognition systems. Last but not least, simulated presentation attacks using generated master faces generally preserved the false matching ability of their original digital forms, thus demonstrating that the existence of master faces poses an actual threat.