  Face anti-spoofing (FAS) and adversarial detection (FAD) have been regarded
as critical technologies to ensure the safety of face recognition systems. As a
consequence of their limited practicality and generalization, some existing
methods aim to devise a framework capable of concurrently detecting both
threats to address the challenge. Nevertheless, these methods still encounter
challenges of insufficient generalization and suboptimal robustness,
potentially owing to the inherent drawback of discriminative models. Motivated
by the rich structural and detailed features of face generative models, we
propose FaceCat which utilizes the face generative model as a pre-trained model
to improve the performance of FAS and FAD. Specifically, FaceCat elaborately
designs a hierarchical fusion mechanism to capture rich face semantic features
of the generative model. These features then serve as a robust foundation for a
lightweight head, designed to execute FAS and FAD tasks simultaneously. As
relying solely on single-modality data often leads to suboptimal performance,
we further propose a novel text-guided multi-modal alignment strategy that
utilizes text prompts to enrich feature representation, thereby enhancing
performance. For fair evaluations, we build a comprehensive protocol with a
wide range of 28 attack types to benchmark the performance. Extensive
experiments validate the effectiveness of FaceCat generalizes significantly
better and obtains excellent robustness against input transformations.
