Devil in Shadow: Attacking NIR-VIS Heterogeneous Face Recognition via Adversarial Shadow

Near infrared-visible (NIR-VIS) heterogeneous face recognition aims to match face identities in cross-modality settings, which has achieved significant development recently. The work on adversarial attack and security issues of the heterogeneous face recognition task is still lacking. Existing adversarial face generation methods can't deploy directly because of the inevitable large modality discrepancy. Besides, the ideal adversarial attacking generated images should maintain both high capabilities and low detectability. Considering the properties of near-infrared face images, our basic idea is to construct adversarial shadows for good stealthiness and high attack capability. In this paper, we propose a novel face adversarial shadow generation framework for NIR-VIS heterogeneous face recognition, which can synthesize fine-crafted lighting conditions containing strong identity attacking ability. Specifically, we design the variance consistency-based symmetric face attacking loss to improve the attacking generalization and the synthesized image quality. Extensive qualitative and quantitative experiments on the public large-scale NIR-VIS heterogeneous face dataset prove the proposed method achieves superior performance compared with the state-of-the-art methods. The source code is publicly available at https://github.com/GEaMU/Devil-in-Shadow.