Face Morphing Attack Detection
7 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Face Morphing Attack Detection
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FRLL-Morphs
Most implemented papers
Vulnerability Analysis of Face Morphing Attacks from Landmarks and Generative Adversarial Networks
Morphing attacks is a threat to biometric systems where the biometric reference in an identity document can be altered.
3D Face Morphing Attacks: Generation, Vulnerability and Detection
To this extent, we introduced a novel approach based on blending 3D face point clouds corresponding to contributory data subjects.
Unsupervised Face Morphing Attack Detection via Self-paced Anomaly Detection
meilfang/spl-mad
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However, given variations in the morphing attacks, the performance of supervised MAD solutions drops significantly due to the insufficient diversity and quantity of the existing MAD datasets.
SYN-MAD 2022: Competition on Face Morphing Attack Detection Based on Privacy-aware Synthetic Training Data
The competition attracted a total of 12 participating teams, both from academia and industry and present in 11 different countries.
Deep Composite Face Image Attacks: Generation, Vulnerability and Detection
Given the face images corresponding to two unique data subjects, the proposed CFIA method will independently generate the segmented facial attributes, then blend them using transparent masks to generate the CFIA samples.
Unveiling the Two-Faced Truth: Disentangling Morphed Identities for Face Morphing Detection
Over time they have become simpler to perform and more realistic, as such, the usage of deep learning systems to detect these attacks has grown.
Face Morphing Attack Detection with Denoising Diffusion Probabilistic Models
MIvanovska/MAD-DDPM
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International Workshop on Biometrics and Forensics (IWBF) 2023
Existing MAD techniques predominantly rely on discriminative models that learn from examples of bona fide and morphed images and, as a result, often exhibit sub-optimal generalization performance when confronted with unknown types of morphing attacks.
