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

Sushma Venkatesh, Raghavendra Ramachandra, Kiran Raja, Luuk Spreeuwers, Raymond Veldhuis, Christoph Busch; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2020, pp. 280-289

The evolving deployment of face recognition system has raised concerns regarding the vulnerability of those systems to various attacks. The morphed face attack involves two different face images via morphing to obtain an attack image face image similar to both original images. The obtained morphed image can easily be verified against both subjects visually and by Face Recognition Systems (FRS). The face morphing attack thus raises a severe concern to various security applications like border control and e-passport unless such attacks are detected and mitigated. In this work, we propose a novel method to reliably detect the morphed face attacks using a new denoising framework. Considering the time complexity and parameterization efforts, we realize the proposed method using a new deep Multi-scale Context Aggregation Network (MS-CAN). Extensive experiments are carried out on three different morphed face image datasets. The Morphed Attack Detection (MAD) performance of the proposed method is also benchmarked against 13 different state-of-the-art techniques using the ISO IEC 30107-3 evaluation metrics. Based on the obtained quantitative results reported, the proposed method has indicated the best performance.