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20232026
most citedExposing the Fake: Effective Diffusion-Generated Images Detection

11 citations · 14 across the 21 of their papers we have counts for

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9 papers · 1 filter

cs.CV2025

LRR-Bench: Left, Right or Rotate? Vision-Language models Still Struggle With Spatial Understanding Tasks

Fei Kong, Jinhao Duan, Kaidi Xu +3

Real-world applications, such as autonomous driving and humanoid robot manipulation, require precise spatial perception. However, it remains underexplored how Vision-Language Model…

cs.CV2025

TruthPrInt: Mitigating Large Vision-Language Models Object Hallucination Via Latent Truthful-Guided Pre-Intervention

Jinhao Duan, Fei Kong, Hao Cheng +6

Object Hallucination (OH) has been acknowledged as one of the major trustworthy challenges in Large Vision-Language Models (LVLMs). Recent advancements in Large Language Models (LL…

cs.CV2024

Transfer Attack for Bad and Good: Explain and Boost Adversarial Transferability across Multimodal Large Language Models

Hao Cheng, Erjia Xiao, Jiayan Yang +8

Multimodal Large Language Models (MLLMs) demonstrate exceptional performance in cross-modality interaction, yet they also suffer adversarial vulnerabilities. In particular, the tra…

cs.CV2024

Unveiling Typographic Deceptions: Insights of the Typographic Vulnerability in Large Vision-Language Model

Hao Cheng, Erjia Xiao, Jindong Gu +6

Large Vision-Language Models (LVLMs) rely on vision encoders and Large Language Models (LLMs) to exhibit remarkable capabilities on various multi-modal tasks in the joint space of…

cs.CV2023

Can Protective Perturbation Safeguard Personal Data from Being Exploited by Stable Diffusion?

Zhengyue Zhao, Jinhao Duan, Kaidi Xu +5

Stable Diffusion has established itself as a foundation model in generative AI artistic applications, receiving widespread research and application. Some recent fine-tuning methods…

cs.CV2023

ACT-Diffusion: Efficient Adversarial Consistency Training for One-step Diffusion Models

Fei Kong, Jinhao Duan, Lichao Sun +6

Though diffusion models excel in image generation, their step-by-step denoising leads to slow generation speeds. Consistency training addresses this issue with single-step sampling…