학술논문

Differential Morphing Attack Detection via Triplet-Based Metric Learning and Artifact Extraction
Document Type
Conference
Source
2024 International Conference of the Biometrics Special Interest Group (BIOSIG) Biometrics Special Interest Group (BIOSIG), 2024 International Conference of the. :1-7 Sep, 2024
Subject
Computing and Processing
Engineering Profession
Photonics and Electrooptics
Measurement
Technological innovation
Face recognition
Biological system modeling
NIST
Feature extraction
Stability analysis
Security
Reliability
Biomedical imaging
Morphing Attack
Differential Morphing Attack Detection (D-MAD)
Feature Fusion
Language
ISSN
1617-5468
Abstract
Face morphing attack has been demonstrated to pose significant security risks to face recognition systems. Therefore, recently, developing reliable Morphing Attack Detection (MAD) techniques has become an important research priority. This paper considers the failure of regular face recognition systems in handling morphed faces from the perspective of identity feature space, arguing that the morphed image creates a “pathway” between the two subjects involved in the morphing generation process. This “pathway” causes automatic face recognition systems to misidentify the morphed face as either contributing subjects. Based on this insight and considering the characteristics of the Differential Morphing Attack Detection (D-MAD) scenario (i.e., both the potential morphing attack image and the trusted live face image are accessible), we propose an end-to-end D-MAD solution based on metric learning with triplet feature separation and artifact analysis. The proposed approach aims to break the “pathway” created by the morphed image in the identity feature space and to incorporate latent artifact features of the potential morphed image for D-MAD. Comparative results on different public benchmarks indicate that the proposed solution demonstrates satisfactory performance against state-of-the-art algorithms.