all InfoSec news
MLSD-GAN -- Generating Strong High Quality Face Morphing Attacks using Latent Semantic Disentanglement
April 22, 2024, 4:11 a.m. | Aravinda Reddy PN, Raghavendra Ramachandra, Krothapalli Sreenivasa Rao, Pabitra Mitra
cs.CR updates on arXiv.org arxiv.org
Abstract: Face-morphing attacks are a growing concern for biometric researchers, as they can be used to fool face recognition systems (FRS). These attacks can be generated at the image level (supervised) or representation level (unsupervised). Previous unsupervised morphing attacks have relied on generative adversarial networks (GANs). More recently, researchers have used linear interpolation of StyleGAN-encoded images to generate morphing attacks. In this paper, we propose a new method for generating high-quality morphing attacks using StyleGAN disentanglement. …
arxiv attacks biometric can cs.cr cs.cv face morphing face recognition gan generated generative high image quality recognition representation researchers semantic systems
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
Financial Crimes Compliance - Senior - Consulting - Location Open
@ EY | New York City, US, 10001-8604
Software Engineer - Cloud Security
@ Neo4j | Malmö
Security Consultant
@ LRQA | Singapore, Singapore, SG, 119963
Identity Governance Consultant
@ Allianz | Sydney, NSW, AU, 2000
Educator, Cybersecurity
@ Brain Station | Toronto
Principal Security Engineer
@ Hippocratic AI | Palo Alto