Pan Zhou

Towards Building Model/Prompt-Transferable Attackers against Large Vision-Language Models

A new perspective of information theory is introduced to investigate LVLMs’ transferable characteristics by exploring the relative dependence between outputs of the LVLM model and input adversarial samples and formulate the complicated calculation of information gain as an estimation problem and incorporate such informative constraints into the adversarial learning process.