9 papers
DeceptionX: From Multimodal Evidence to Explainable Deception Detection
Jiayu Zhang, Shuo Ye, Jiajian Huang +8
Deception detection is a critical and highly challenging task within affective computing and behavioral analysis. Existing deep learning methods typically treat this task as a stra…
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…
Seg-Agent: Test-Time Multimodal Reasoning for Training-Free Language-Guided Segmentation
Chao Hao, Jun Xu, Ji Du +6
Language-guided segmentation transcends the scope limitations of traditional semantic segmentation, enabling models to segment arbitrary target regions based on natural language in…
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…
DecepGPT: Schema-Driven Deception Detection with Multicultural Datasets and Robust Multimodal Learning
Jiajian Huang, Dongliang Zhu, Zitong YU +4
Multimodal deception detection aims to identify deceptive behavior by analyzing audiovisual cues for forensics and security. In these high-stakes settings, investigators need verif…
PA-FAS: Towards Interpretable and Generalizable Multimodal Face Anti-Spoofing via Path-Augmented Reinforcement Learning
Yingjie Ma, Xun Lin, Yong Xu +2
Face anti-spoofing (FAS) has recently advanced in multimodal fusion, cross-domain generalization, and interpretability. With large language models and reinforcement learning (RL),…
SVC 2025: the First Multimodal Deception Detection Challenge
Xun Lin, Xiaobao Guo, Taorui Wang +5
Deception detection is a critical task in real-world applications such as security screening, fraud prevention, and credibility assessment. While deep learning methods have shown p…
Purify then Guide: Rethinking Domain Generalization for Multimodal Face Anti-Spoofing
Yingjie Ma, Xun Lin, Zitong Yu +7
Face Anti-Spoofing (FAS) is essential for the security of facial recognition systems in diverse scenarios such as payment processing and surveillance. Current multimodal FAS method…
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…