This research paper introduces LADIMO, a new method for generating realistic face morphs by inverting biometric templates using Latent Diffusion Models, posing a significant threat to Facial Recognition Systems (FRS) and highlighting the need for improved security measures.
The proposed MLSD-GAN method can generate high-quality face morphing attacks that pose a significant threat to deep learning-based face recognition systems.
Diffusion Morphs (DiM) can be optimized for faster creation with reduced Network Function Evaluations (NFE).
Fast-DiM proposes a novel morphing method to reduce Network Function Evaluations (NFE) while maintaining performance in face recognition systems.