6 citations · 7 across the 12 of their papers we have counts for
13 papers
GBU-Palm: A Multimodal Video Dataset and Benchmark for Palm Presentation Attack Detection
Yingjie Ma, Zitong Yu, Wei Jia +2
Existing palm presentation attack detection (PAD) datasets are often limited by static imagery, restricted acquisition conditions, or insufficient multimodal video data, hindering…
Text-Guided Multimodal Unified Industrial Anomaly Detection
Zewen Li, Shuo Ye, Zitong Yu +2
Industrial anomaly detection based on RGB-3D multimodal data has emerged as a mainstream paradigm for intelligent quality inspection. However, existing unsupervised methods suffer…
Enhancing Adversarial Transferability by Balancing Exploration and Exploitation with Gradient-Guided Sampling
Zenghao Niu, Weicheng Xie, Siyang Song +3
Adversarial attacks present a critical challenge to deep neural networks' robustness, particularly in transfer scenarios across different model architectures. However, the transfer…
IAD-GPT: Advancing Visual Knowledge in Multimodal Large Language Model for Industrial Anomaly Detection
Zewen Li, Zitong Yu, Qilang Ye +3
The robust causal capability of Multimodal Large Language Models (MLLMs) hold the potential of detecting defective objects in Industrial Anomaly Detection (IAD). However, most trad…
Agent4FaceForgery: Multi-Agent LLM Framework for Realistic Face Forgery Detection
Yingxin Lai, Zitong Yu, Jun Wang +3
Face forgery detection faces a critical challenge: a persistent gap between offline benchmarks and real-world efficacy,which we attribute to the ecological invalidity of training d…
Mitigating Group-Level Fairness Disparities in Federated Visual Language Models
Chaomeng Chen, Zitong Yu, Junhao Dong +4
Visual language models (VLMs) have shown remarkable capabilities in multimodal tasks but face challenges in maintaining fairness across demographic groups, particularly when deploy…
Distilled Transformers with Locally Enhanced Global Representations for Face Forgery Detection
Yaning Zhang, Qiufu Li, Zitong Yu +1
Face forgery detection (FFD) is devoted to detecting the authenticity of face images. Although current CNN-based works achieve outstanding performance in FFD, they are susceptible…
BIG-MoE: Bypass Isolated Gating MoE for Generalized Multimodal Face Anti-Spoofing
Yingjie Ma, Zitong Yu, Xun Lin +2
In the domain of facial recognition security, multimodal Face Anti-Spoofing (FAS) is essential for countering presentation attacks. However, existing technologies encounter challen…
CA-Edit: Causality-Aware Condition Adapter for High-Fidelity Local Facial Attribute Editing
Xiaole Xian, Xilin He, Zenghao Niu +5
For efficient and high-fidelity local facial attribute editing, most existing editing methods either require additional fine-tuning for different editing effects or tend to affect…
MFCLIP: Multi-modal Fine-grained CLIP for Generalizable Diffusion Face Forgery Detection
Yaning Zhang, Tianyi Wang, Zitong Yu +3
The rapid development of photo-realistic face generation methods has raised significant concerns in society and academia, highlighting the urgent need for robust and generalizable…
DDAP: Dual-Domain Anti-Personalization against Text-to-Image Diffusion Models
Jing Yang, Runping Xi, Yingxin Lai +2
Diffusion-based personalized visual content generation technologies have achieved significant breakthroughs, allowing for the creation of specific objects by just learning from a f…
GM-DF: Generalized Multi-Scenario Deepfake Detection
Yingxin Lai, Zitong Yu, Jing Yang +3
Existing face forgery detection usually follows the paradigm of training models in a single domain, which leads to limited generalization capacity when unseen scenarios and unknown…
GenFace: A Large-Scale Fine-Grained Face Forgery Benchmark and Cross Appearance-Edge Learning
Yaning Zhang, Zitong Yu, Tianyi Wang +4
The rapid advancement of photorealistic generators has reached a critical juncture where the discrepancy between authentic and manipulated images is increasingly indistinguishable.…