Detecting Morphed Face Attacks Using Residual Noise from Deep
Multi-scale Context Aggregation Network
Abstract
Along with the deployment of the Face Recognition Systems
(FRS), concerns were raised related to the vulnerability
of those systems towards various attacks including morphed
attacks. The morphed face attack involves two different
face images in order to obtain via a morphing process
a resulting attack image, which is sufficiently similar
to both contributing data subjects. The obtained morphed
image can successfully be verified against both subjects visually
(by a human expert) and by a commercial FRS. The
face morphing attack poses a severe security risk to the
e-passport issuance process and to applications like border
control, unless such attacks are detected and mitigated.
In this work, we propose a new method to reliably detect
a morphed face attack using a newly designed denoising
framework. To this end, we design and introduce a new
deep Multi-scale Context Aggregation Network (MS-CAN)
to obtain denoised images, which is subsequently used to
determine if an image is morphed or not. Extensive experiments
are carried out on three different morphed face image
datasets. The Morphing Attack Detection (MAD) performance
of the proposed method is also benchmarked against
14 different state-of-the-art techniques using the ISO-IEC
30107-3 evaluation metrics. Based on the obtained quantitative
results, the proposed method has indicated the best
performance on all three datasets and also on cross-dataset
experiments.
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