April 11, 2024, 4:44 a.m. | Richard E. Neddo, Zander W. Blasingame, Chen Liu

cs.CV updates on arXiv.org arxiv.org

arXiv:2404.06559v1 Announce Type: new
Abstract: Face morphing attacks present an emerging threat to the face recognition system. On top of that, printing and scanning the morphed images could obscure the artifacts generated during the morphing process, which makes morphed image detection even harder. In this work, we investigate the impact that printing and scanning has on morphing attacks through a series of heterogeneous tests. Our experiments show that we can increase the possibility of a false match by up to …

abstract arxiv attacks cs.cv detection evaluation face face recognition generated image image detection images impact printing process recognition threat type work

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Senior Data Engineer

@ Quantexa | Sydney, New South Wales, Australia

Staff Analytics Engineer

@ Warner Bros. Discovery | NY New York 230 Park Avenue South