3 citations · 12 across the 33 of their papers we have counts for
16 papers · 1 filter
SPIN: Image Immunization Against Diffusion Editing via Single-Step Projection in Stochastic Neighborhoods
Fengming Gu, Jie Zhang, Zhongqi Wang +3
Diffusion models have greatly advanced instruction-guided image editing, while also raising concerns about unauthorized image manipulation. Image immunization addresses this risk b…
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…
Beyond Saying Less: Fine-Grained Alignment for Informative and Faithful Vision-Language Models
Xingming Long, Jie Zhang, Yuecong Min +2
Object hallucination remains a major challenge for large vision-language models. While off-policy preference optimization proves to be an effective solution, on-policy reinforcemen…
V-Gym: Enhancing Agentic Visual Reasoning via Skill-Data Co-Evolution
Bei Yan, Yuecong Min, Jie Zhang +3
Advances in multimodal understanding, reasoning, and tool use enable agents to tackle increasingly complex visual reasoning tasks. By distilling past execution experience into reus…
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…
One Attack to Fool Them All: Highly Transferable Black-Box Adversarial Attacks on Frontier MLLMs
Sen Nie, Jie Zhang, Zhongqi Wang +2
Adversarial attacks have long posed a fundamental threat to machine learning systems. As multimodal large language models (MLLMs) rapidly evolve and become widely deployed, assessi…
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…
DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs
Anqi Li, Jie Zhang, Zhongqi Wang +4
While Large Vision-Language Models (LVLMs) demonstrate remarkable capabilities, they remain highly susceptible to embedded social biases. Existing bias evaluation protocols predomi…
EntropyScan: Towards Model-level Backdoor Detection in LVLMs via Visual Attention Entropy
Xuanyu Ge, Zhongqi Wang, Jie Zhang +2
Large Vision-Language Models (LVLMs) have demonstrated remarkable capabilities across various tasks, yet they remain vulnerable to backdoor attacks. Existing defense methods predom…
Component-Based Out-of-Distribution Detection
Wenrui Liu, Hong Chang, Ruibing Hou +2
Out-of-Distribution (OOD) detection requires sensitivity to subtle shifts without overreacting to natural In-Distribution (ID) diversity. However, from the viewpoint of detection g…
EgoMotion: Hierarchical Reasoning and Diffusion for Egocentric Vision-Language Motion Generation
Ruibing Hou, Mingyue Zhou, Yuwei Gui +5
Faithfully modeling human behavior in dynamic environments is a foundational challenge for embodied intelligence. While conditional motion synthesis has achieved significant advanc…
ACT Now: Preempting LVLM Hallucinations via Adaptive Context Integration
Bei Yan, Yuecong Min, Jie Zhang +2
Large Vision-Language Models (LVLMs) frequently suffer from severe hallucination issues. Existing mitigation strategies predominantly rely on isolated, single-step states to enhanc…
INFACT: A Diagnostic Benchmark for Induced Faithfulness and Factuality Hallucinations in Video-LLMs
Junqi Yang, Yuecong Min, Jie Zhang +2
Despite rapid progress, Video Large Language Models (Video-LLMs) remain unreliable due to hallucinations, which are outputs that contradict either video evidence (faithfulness) or…
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…