57 citations · 125 across the 36 of their papers we have counts for
7 papers · 1 filter
BadAction: Backdoor Attacks on Interactive Video Generation via Action-Guided Triggers
Zhihang Wu, Zhongqi Wang, Jie Zhang +3
Interactive video generation (IVG) models have achieved remarkable progress in producing controllable visual content guided by user-defined actions, yet their security vulnerabilit…
Backdoor as Probe: Test-Time Adversarial Defense for CLIP
Zhongqi Wang, Jie Zhang, Nie Sen +3
Test-time adversarial defense improves the robustness of vision-language foundation models such as CLIP without retraining. However, adversarial activation shifts are typically tre…
Dual Adversarial Fine-tuning for Enhancing Robustness of Large Vision Language Model
Sibo Wang, Jie Zhang, Shiguang Shan +2
While Large Vision-Language Models (LVLMs), represented by LLaVA and GPT-4V, have demonstrated remarkable capabilities, their visual inputs remain vulnerable to adversarial attacks…
TRACE: Structure-Aware Character Encoding for Robust and Generalizable Document Watermarking
Jiale Meng, Jie Zhang, Runyi Hu +3
We propose TRACE, a structure-aware framework leveraging diffusion models for localized character encoding to embed data. Unlike existing methods that rely on edge features or pre-…
What Makes VLMs Robust? Towards Reconciling Robustness and Accuracy in Vision-Language Models
Sen Nie, Jie Zhang, Zhongqi Wang +3
Achieving adversarial robustness in Vision-Language Models (VLMs) inevitably compromises accuracy on clean data, presenting a long-standing and challenging trade-off. In this work,…
Neural Gate: Mitigating Privacy Risks in LVLMs via Neuron-Level Gradient Gating
Xiangkui Cao, Jie Zhang, Meina Kan +2
Large Vision-Language Models (LVLMs) have shown remarkable potential across a wide array of vision-language tasks, leading to their adoption in critical domains such as finance and…
Contrastive Spectral Rectification: Test-Time Defense towards Zero-shot Adversarial Robustness of CLIP
Sen Nie, Jie Zhang, Zhuo Wang +2
Vision-language models (VLMs) such as CLIP have demonstrated remarkable zero-shot generalization, yet remain highly vulnerable to adversarial examples (AEs). While test-time defens…