Detecting Morphed Face Attacks Using Residual Noise from Deep Multi-scale Context Aggregation Network

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.