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