
Attack-agnostic image features, extracted from large vision models pretrained on massive datasets of real images, show significant potential for detecting morphing attacks on face recognition systems, often outperforming traditional supervised methods and demonstrating promising generalization capabilities across different attack types, source datasets, and even print-scan domains.


coremsg

evaluating-the-effectiveness-of-attack-agnostic-features-for-detecting-morphing-attacks-on-face-recognition-systems


Evaluating the Effectiveness of Attack-Agnostic Features for Detecting Morphing Attacks on Face Recognition Systems


title_rewrite


Leveraging deep face embeddings can significantly improve the attack potential of automated face morphing attacks by enabling efficient selection of morph pairs, while also providing a robust alternative for detecting such attacks.


automated-face-morphing-attacks-leveraging-deep-embeddings-for-efficient-pair-selection-and-improved-detection


Automated Face Morphing Attacks: Leveraging Deep Embeddings for Efficient Pair Selection and Improved Detection
