3 citations · 5 across the 9 of their papers we have counts for
6 papers · 1 filter
Learning Representation and Synergy Invariances: A Povable Framework for Generalized Multimodal Face Anti-Spoofing
Xun Lin, Shuai Wang, Yi Yu +6
Multimodal Face Anti-Spoofing (FAS) methods, which integrate multiple visual modalities, often suffer even more severe performance degradation than unimodal FAS when deployed in un…
Towards Data-Centric Face Anti-Spoofing: Improving Cross-domain Generalization via Physics-based Data Synthesis
Rizhao Cai, Cecelia Soh, Zitong Yu +3
Face Anti-Spoofing (FAS) research is challenged by the cross-domain problem, where there is a domain gap between the training and testing data. While recent FAS works are mainly mo…
Suppress and Rebalance: Towards Generalized Multi-Modal Face Anti-Spoofing
Xun Lin, Shuai Wang, Rizhao Cai +5
Face Anti-Spoofing (FAS) is crucial for securing face recognition systems against presentation attacks. With advancements in sensor manufacture and multi-modal learning techniques,…
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…
Exposing Image Splicing Traces in Scientific Publications via Uncertainty-guided Refinement
Xun Lin, Wenzhong Tang, Haoran Wang +4
Recently, a surge in scientific publications suspected of image manipulation has led to numerous retractions, bringing the issue of image integrity into sharp focus. Although resea…
S-Adapter: Generalizing Vision Transformer for Face Anti-Spoofing with Statistical Tokens
Rizhao Cai, Zitong Yu, Chenqi Kong +4
Face Anti-Spoofing (FAS) aims to detect malicious attempts to invade a face recognition system by presenting spoofed faces. State-of-the-art FAS techniques predominantly rely on de…