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cs.CV2026

Arc2Morph: Identity-Preserving Facial Morphing with Arc2Face

Nicolò Di Domenico, Annalisa Franco, Matteo Ferrara +1

Face morphing attacks are widely recognized as one of the most challenging threats to face recognition systems used in electronic identity documents. These attacks exploit a critic…

cs.CV2024

ONOT: a High-Quality ICAO-compliant Synthetic Mugshot Dataset

Nicolò Di Domenico, Guido Borghi, Annalisa Franco +1

Nowadays, state-of-the-art AI-based generative models represent a viable solution to overcome privacy issues and biases in the collection of datasets containing personal informatio…

cs.CV2024

Dealing with Subject Similarity in Differential Morphing Attack Detection

Nicolò Di Domenico, Guido Borghi, Annalisa Franco +1

The advent of morphing attacks has posed significant security concerns for automated Face Recognition systems, raising the pressing need for robust and effective Morphing Attack De…

cs.CV2024

V-MAD: Video-based Morphing Attack Detection in Operational Scenarios

Guido Borghi, Annalisa Franco, Nicolò Di Domenico +2

In response to the rising threat of the face morphing attack, this paper introduces and explores the potential of Video-based Morphing Attack Detection (V-MAD) systems in real-worl…

cs.CV2024

SDFR: Synthetic Data for Face Recognition Competition

Hatef Otroshi Shahreza, Christophe Ecabert, Anjith George +25

Large-scale face recognition datasets are collected by crawling the Internet and without individuals' consent, raising legal, ethical, and privacy concerns. With the recent advance…