3 papers
cs.CV2023
MorDIFF: Recognition Vulnerability and Attack Detectability of Face Morphing Attacks Created by Diffusion Autoencoders
Naser Damer, Meiling Fang, Patrick Siebke +3
Investigating new methods of creating face morphing attacks is essential to foresee novel attacks and help mitigate them. Creating morphing attacks is commonly either performed on…
cs.CV2022
SFace: Privacy-friendly and Accurate Face Recognition using Synthetic Data
Fadi Boutros, Marco Huber, Patrick Siebke +2
Recent deep face recognition models proposed in the literature utilized large-scale public datasets such as MS-Celeb-1M and VGGFace2 for training very deep neural networks, achievi…
cs.CV2021
PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation
Fadi Boutros, Patrick Siebke, Marcel Klemt +3
Deep neural networks have rapidly become the mainstream method for face recognition (FR). However, this limits the deployment of such models that contain an extremely large number…