3 citations · 3 across the 5 of their papers we have counts for
5 papers
Spoofing-aware Prompt Learning for Unified Physical-Digital Facial Attack Detection
Jiabao Guo, Yadian Wang, Hui Ma +7
Real-world face recognition systems are vulnerable to both physical presentation attacks (PAs) and digital forgery attacks (DFs). We aim to achieve comprehensive protection of biom…
Wi-CBR: Salient-aware Adaptive WiFi Sensing for Cross-domain Behavior Recognition
Ruobei Zhang, Shengeng Tang, Huan Yan +2
The challenge in WiFi-based cross-domain Behavior Recognition lies in the significant interference of domain-specific signals on gesture variation. However, previous methods allevi…
Domain Generalization for Face Anti-spoofing via Content-aware Composite Prompt Engineering
Jiabao Guo, Ajian Liu, Yunfeng Diao +5
The challenge of Domain Generalization (DG) in Face Anti-Spoofing (FAS) is the significant interference of domain-specific signals on subtle spoofing clues. Recently, some CLIP-bas…
SUEDE:Shared Unified Experts for Physical-Digital Face Attack Detection Enhancement
Zuying Xie, Changtao Miao, Ajian Liu +4
Face recognition systems are vulnerable to physical attacks (e.g., printed photos) and digital threats (e.g., DeepFake), which are currently being studied as independent visual tas…
Feature-Filter: Detecting Adversarial Examples through Filtering off Recessive Features
Hui Liu, Bo Zhao, Minzhi Ji +3
Deep neural networks (DNNs) are under threat from adversarial example attacks. The adversary can easily change the outputs of DNNs by adding small well-designed perturbations to in…