Detection of Adversarial Facial Accessory Presentation Attacks Using Local Face Differential

Abstract

To counter adversarial facial accessory presentation attacks (PAs), a detection method based on local face differential is proposed in this article. It extracts the local face differential features from a suspected face image and a reference face image, and then adaptively fuses the differential features of different local face regions to detect adversarial facial accessory PAs. Meanwhile, the principle of the proposed method is explained by theoretically investigating the local facial differences between a bona fide presentation and an adversarial facial accessory PA when they are compared with a reference face image. To evaluate the proposed method, this article builds a database with different adversarial examples (AEs), presentation attack instruments (PAIs), illumination conditions, and cameras. The experimental results show that it can effectively distinguish between adversarial facial accessory PAs and bona fide presentations, and it has good generalization ability to unseen AEs, PAIs, illumination conditions, and cameras. Moreover, it outperforms the existing AE detection and presentation attack detection methods in detecting adversarial facial accessory PAs.