Unsupervised Face Morphing Attack Detection via Self-paced Anomaly Detection. (arXiv:2208.05787v1 [cs.CV])

The supervised-learning-based morphing attack detection (MAD) solutions
achieve outstanding success in dealing with attacks from known morphing
techniques and known data sources. 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. To
address this concern, we propose a completely unsupervised MAD solution via
self-paced anomaly detection (SPL-MAD) by leveraging the existing large-scale
face recognition (FR) datasets and the unsupervised nature of convolutional …

anomaly anomaly detection arxiv cv detection face unsupervised