
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.


coremsg

ladimo-a-novel-approach-to-face-morphing-using-latent-diffusion-models-for-biometric-template-inversion


LADIMO: A Novel Approach to Face Morphing Using Latent Diffusion Models for Biometric Template Inversion


title_rewrite


The proposed MLSD-GAN method can generate high-quality face morphing attacks that pose a significant threat to deep learning-based face recognition systems.


generating-highly-effective-face-morphing-attacks-using-latent-semantic-disentanglement-in-stylegan


Generating Highly Effective Face Morphing Attacks using Latent Semantic Disentanglement in StyleGAN



Diffusion Morphs (DiM) can be optimized for faster creation with reduced Network Function Evaluations (NFE).


fast-dim-towards-fast-diffusion-morphs-in-face-recognition-systems


Fast-DiM: Towards Fast Diffusion Morphs in Face Recognition Systems



Fast-DiM proposes a novel morphing method to reduce Network Function Evaluations (NFE) while maintaining performance in face recognition systems.
