  Face Recognition (FR) systems can suffer from physical (i.e., print photo)
and digital (i.e., DeepFake) attacks. However, previous related work rarely
considers both situations at the same time. This implies the deployment of
multiple models and thus more computational burden. The main reasons for this
lack of an integrated model are caused by two factors: (1) The lack of a
dataset including both physical and digital attacks with ID consistency which
means the same ID covers the real face and all attack types; (2) Given the
large intra-class variance between these two attacks, it is difficult to learn
a compact feature space to detect both attacks simultaneously. To address these
issues, we collect a Unified physical-digital Attack dataset, called
UniAttackData. The dataset consists of $1,800$ participations of 2 and 12
physical and digital attacks, respectively, resulting in a total of 29,706
videos. Then, we propose a Unified Attack Detection framework based on
Vision-Language Models (VLMs), namely UniAttackDetection, which includes three
main modules: the Teacher-Student Prompts (TSP) module, focused on acquiring
unified and specific knowledge respectively; the Unified Knowledge Mining (UKM)
module, designed to capture a comprehensive feature space; and the Sample-Level
Prompt Interaction (SLPI) module, aimed at grasping sample-level semantics.
These three modules seamlessly form a robust unified attack detection
framework. Extensive experiments on UniAttackData and three other datasets
demonstrate the superiority of our approach for unified face attack detection.
