3 citations · 3 across the 6 of their papers we have counts for
4 papers · 1 filter
FAS-R1: A Unified Multi-Task MLLM for Reasoning Face Anti-Spoofing
Hongyang Wang, Yichen Shi, Hongrui Li +3
Face anti-spoofing (FAS) is increasingly expected to provide not only bona fide/spoof decisions, but also attack semantics and image-grounded evidence for human inspection. Existin…
UniShield: Unified Face Attack Detection via KG-Informed Multimodal Reasoning
Hongrui Li, Yichen Shi, Hongyang Wang +4
Unified face attack detection (UAD) requires recognizing physical spoofing and digital forgery within a shared decision space, yet existing discriminative or prompt-based methods l…
FaceShield: Explainable Face Anti-Spoofing with Multimodal Large Language Models
Hongyang Wang, Yichen Shi, Zhuofu Tao +7
Face anti-spoofing (FAS) is crucial for protecting facial recognition systems from presentation attacks. Previous methods approached this task as a classification problem, lacking…
SHIELD : An Evaluation Benchmark for Face Spoofing and Forgery Detection with Multimodal Large Language Models
Yichen Shi, Yuhao Gao, Yingxin Lai +7
Multimodal large language models (MLLMs) have demonstrated strong capabilities in vision-related tasks, capitalizing on their visual semantic comprehension and reasoning capabiliti…