6 citations · 9 across the 21 of their papers we have counts for
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AdvDreamer Unveils: Are Vision-Language Models Truly Ready for Real-World 3D Variations?
Shouwei Ruan, Hanqing Liu, Yao Huang +5
Vision Language Models (VLMs) have exhibited remarkable generalization capabilities, yet their robustness in dynamic real-world scenarios remains largely unexplored. To systematica…
Real-world Adversarial Defense against Patch Attacks based on Diffusion Model
Xingxing Wei, Caixin Kang, Yinpeng Dong +4
Adversarial patches present significant challenges to the robustness of deep learning models, making the development of effective defenses become critical for real-world applicatio…
Omniview-Tuning: Boosting Viewpoint Invariance of Vision-Language Pre-training Models
Shouwei Ruan, Yinpeng Dong, Hanqing Liu +3
Vision-Language Pre-training (VLP) models like CLIP have achieved remarkable success in computer vision and particularly demonstrated superior robustness to distribution shifts of…
Exploring the Transferability of Visual Prompting for Multimodal Large Language Models
Yichi Zhang, Yinpeng Dong, Siyuan Zhang +3
Although Multimodal Large Language Models (MLLMs) have demonstrated promising versatile capabilities, their performance is still inferior to specialized models on downstream tasks,…
FaceCat: Enhancing Face Recognition Security with a Unified Diffusion Model
Jiawei Chen, Xiao Yang, Yinpeng Dong +2
Face anti-spoofing (FAS) and adversarial detection (FAD) have been regarded as critical technologies to ensure the safety of face recognition systems. However, due to limited pract…
Embodied Active Defense: Leveraging Recurrent Feedback to Counter Adversarial Patches
Lingxuan Wu, Xiao Yang, Yinpeng Dong +3
The vulnerability of deep neural networks to adversarial patches has motivated numerous defense strategies for boosting model robustness. However, the prevailing defenses depend on…