BMIM: Generating Adversarial Attack on Face Recognition via Binary Mask

Abstract

Face recognition has received interest among researchers due to the model vulnerabilities towards adversarial threats, which are imperceptible to human eye. In this research, we proposed binary mask iterative method (BMIM). In this method, we generate the attack by occluding the face landmark to fool the face recognition model. To conduct the extensive experiment, we used three face recognition models i.e. MobileFace, MobileNet, and SphereFace on Labeled Face in the Wild (LFW) dataset. We evaluate the robustness of these models against dodging and impersonate black-box attacks under $L_{\\infty}$ norms and improve the transferability of existing attacks. The experimental results show that our method achieved desired results.