27 citations · 29 across the 8 of their papers we have counts for
5 papers · 1 filter
KVSmooth: Mitigating Hallucination in Multi-modal Large Language Models through Key-Value Smoothing
Siyu Jiang, Feiyang Chen, Xiaojin Zhang +1
Despite the significant progress of Multimodal Large Language Models (MLLMs) across diverse tasks, hallucination -- corresponding to the generation of visually inconsistent objects…
ViT-EnsembleAttack: Augmenting Ensemble Models for Stronger Adversarial Transferability in Vision Transformers
Hanwen Cao, Haobo Lu, Xiaosen Wang +1
Ensemble-based attacks have been proven to be effective in enhancing adversarial transferability by aggregating the outputs of models with various architectures. However, existing…
VisCRA: A Visual Chain Reasoning Attack for Jailbreaking Multimodal Large Language Models
Bingrui Sima, Linhua Cong, Wenxuan Wang +1
The emergence of Multimodal Large Language Models (MLRMs) has enabled sophisticated visual reasoning capabilities by integrating reinforcement learning and Chain-of-Thought (CoT) s…
Boosting Adversarial Transferability through Enhanced Momentum
Xiaosen Wang, Jiadong Lin, Han Hu +2
Deep learning models are known to be vulnerable to adversarial examples crafted by adding human-imperceptible perturbations on benign images. Many existing adversarial attack metho…
Admix: Enhancing the Transferability of Adversarial Attacks
Xiaosen Wang, Xuanran He, Jingdong Wang +1
Deep neural networks are known to be extremely vulnerable to adversarial examples under white-box setting. Moreover, the malicious adversaries crafted on the surrogate (source) mod…