1 citations · 2 across the 11 of their papers we have counts for
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From Intuition to Investigation: A Tool-Augmented Reasoning MLLM Framework for Generalizable Face Anti-Spoofing
Haoyuan Zhang, Keyao Wang, Guosheng Zhang +11
Face recognition remains vulnerable to presentation attacks, calling for robust Face Anti-Spoofing (FAS) solutions. Recent MLLM-based FAS methods reformulate the binary classificat…
CTForensics: A Comprehensive Dataset and Method for AI-Generated CT Image Detection
Yiheng Li, Zichang Tan, Guoqing Xu +3
Recent advances in generative AI have made synthetic Computed Tomography (CT) images increasingly realistic, enabling promising applications in medical data augmentation while rais…
Veritas: Generalizable Deepfake Detection via Pattern-Aware Reasoning
Hao Tan, Jun Lan, Zichang Tan +7
Deepfake detection remains a formidable challenge due to the complex and evolving nature of fake content in real-world scenarios. However, existing academic benchmarks suffer from…
Mixture-of-Attack-Experts with Class Regularization for Unified Physical-Digital Face Attack Detection
Shunxin Chen, Ajian Liu, Junze Zheng +4
Facial recognition systems in real-world scenarios are susceptible to both digital and physical attacks. Previous methods have attempted to achieve classification by learning a com…
FA^{3}-CLIP: Frequency-Aware Cues Fusion and Attack-Agnostic Prompt Learning for Unified Face Attack Detection
Yongze Li, Ning Li, Ajian Liu +7
Facial recognition systems are vulnerable to physical (e.g., printed photos) and digital (e.g., DeepFake) face attacks. Existing methods struggle to simultaneously detect physical…
La-SoftMoE CLIP for Unified Physical-Digital Face Attack Detection
Hang Zou, Chenxi Du, Hui Zhang +4
Facial recognition systems are susceptible to both physical and digital attacks, posing significant security risks. Traditional approaches often treat these two attack types separa…
CRL: Content and Context Representation Learning for Gloss-free Sign Language Translation and Retrieval
Zhigang Chen, Benjia Zhou, Yiqing Huang +6
Sign Language Representation Learning (SLRL) is crucial for a range of sign language-related downstream tasks such as Sign Language Translation (SLT) and Sign Language Retrieval (S…
Unified Physical-Digital Attack Detection Challenge
Haocheng Yuan, Ajian Liu, Junze Zheng +6
Face Anti-Spoofing (FAS) is crucial to safeguard Face Recognition (FR) Systems. In real-world scenarios, FRs are confronted with both physical and digital attacks. However, existin…
CFPL-FAS: Class Free Prompt Learning for Generalizable Face Anti-spoofing
Ajian Liu, Shuai Xue, Jianwen Gan +5
Domain generalization (DG) based Face Anti-Spoofing (FAS) aims to improve the model's performance on unseen domains. Existing methods either rely on domain labels to align domain-i…
Unified Physical-Digital Face Attack Detection
Hao Fang, Ajian Liu, Haocheng Yuan +8
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…