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аналитика - Face Morphing Attacks
LADIMO: A Novel Approach to Face Morphing Using Latent Diffusion Models for Biometric Template Inversion
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.
Generating Highly Effective Face Morphing Attacks using Latent Semantic Disentanglement in StyleGAN
The proposed MLSD-GAN method can generate high-quality face morphing attacks that pose a significant threat to deep learning-based face recognition systems.
Fast-DiM: Towards Fast Diffusion Morphs in Face Recognition Systems
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 proposes a novel morphing method to reduce Network Function Evaluations (NFE) while maintaining performance in face recognition systems.
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