Following the advent of the Artificial Intelligence (AI) era of large models,
Multimodal Large Language Models (MLLMs) with the ability to understand
cross-modal interactions between vision and text have attracted wide attention.
Adversarial examples with human-imperceptible perturbation are shown to possess
a characteristic known as transferability, which means that a perturbation
generated by one model could also mislead another different model. Augmenting
the diversity in input data is one of the most significant methods for
enhancing adversarial transferability. This method has been certified as a way
to significantly enlarge the threat impact under black-box conditions. Research
works also demonstrate that MLLMs can be exploited to generate adversarial
examples in the white-box scenario. However, the adversarial transferability of
such perturbations is quite limited, failing to achieve effective black-box
attacks across different models. In this paper, we propose the
Typographic-based Semantic Transfer Attack (TSTA), which is inspired by: (1)
MLLMs tend to process semantic-level information; (2) Typographic Attack could
effectively distract the visual information captured by MLLMs. In the scenarios
of Harmful Word Insertion and Important Information Protection, our TSTA
demonstrates superior performance.

本文提出了基于字体的语义转移攻击（TSTA），通过干扰多模态大型语言模型（MLLMs）所捕捉的视觉信息，以在有害词语插入和重要信息保护场景中展现优异性能。