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20232026
most citedEvil Geniuses: Delving into the Safety of LLM-based Agents

6 citations · 9 across the 21 of their papers we have counts for

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Showing 2024Show all

10 papers · 1 filter

cs.CV2024

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…

cs.CV2024

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…

cs.CL2024

MultiTrust: A Comprehensive Benchmark Towards Trustworthy Multimodal Large Language Models

Yichi Zhang, Yao Huang, Yitong Sun +10

Despite the superior capabilities of Multimodal Large Language Models (MLLMs) across diverse tasks, they still face significant trustworthiness challenges. Yet, current literature…

cs.CR2024★ 2 cited

Membership Inference on Text-to-Image Diffusion Models via Conditional Likelihood Discrepancy

Shengfang Zhai, Huanran Chen, Yinpeng Dong +5

Text-to-image diffusion models have achieved tremendous success in the field of controllable image generation, while also coming along with issues of privacy leakage and data copyr…

cs.CV2024

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…

cs.CV2024

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,…

cs.CV2024

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…

cs.CV2024

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…

cs.CR2024

BSPA: Exploring Black-box Stealthy Prompt Attacks against Image Generators

Yu Tian, Xiao Yang, Yinpeng Dong +3

Extremely large image generators offer significant transformative potential across diverse sectors. It allows users to design specific prompts to generate realistic images through…

cs.CR2024

Discovering Universal Semantic Triggers for Text-to-Image Synthesis

Shengfang Zhai, Weilong Wang, Jiajun Li +3

Recently text-to-image models have gained widespread attention in the community due to their controllable and high-quality generation ability. However, the robustness of such model…