5 papers
LingoLoop Attack: Trapping MLLMs via Linguistic Context and State Entrapment into Endless Loops
Jiyuan Fu, Kaixun Jiang, Lingyi Hong +5
Multimodal Large Language Models (MLLMs) have shown great promise but require substantial computational resources during inference. Attackers can exploit this by inducing excessive…
Improving Adversarial Transferability with Neighbourhood Gradient Information
Haijing Guo, Jiafeng Wang, Zhaoyu Chen +5
Deep neural networks (DNNs) are known to be susceptible to adversarial examples, leading to significant performance degradation. In black-box attack scenarios, a considerable attac…
Dynamic Semantic-Aware Correlation Modeling for UAV Tracking
Xinyu Zhou, Tongxin Pan, Lingyi Hong +5
UAV tracking can be widely applied in scenarios such as disaster rescue, environmental monitoring, and logistics transportation. However, existing UAV tracking methods predominantl…
Boosting Adversarial Transferability with Spatial Adversarial Alignment
Zhaoyu Chen, Haijing Guo, Kaixun Jiang +6
Deep neural networks are vulnerable to adversarial examples that exhibit transferability across various models. Numerous approaches are proposed to enhance the transferability of a…
Enhancing Diffusion-based Unrestricted Adversarial Attacks via Adversary Preferences Alignment
Kaixun Jiang, Zhaoyu Chen, Haijing Guo +6
Preference alignment in diffusion models has primarily focused on benign human preferences (e.g., aesthetic). In this paper, we propose a novel perspective: framing unrestricted ad…