多模态大语言模型视觉定位的对抗鲁棒性
Adversarial Robustness for Visual Grounding of Multimodal Large Language Models
AI总结：
该研究针对多模态大语言模型视觉定位的对抗鲁棒性空白，以指代表达理解为任务提出三种对抗攻击范式，实验验证其攻击有效性，为新型攻击设计和鲁棒性提升提供了新视角与强基线。
摘要：
多模态大语言模型（MLLMs）近期在包括视觉定位能力在内的各类视觉-语言任务中取得了性能提升。然而，MLLMs中视觉定位的对抗鲁棒性仍未被探索。为填补这一空白，我们以指代表达理解（REC）作为视觉定位的示例任务，提出了以下三种对抗攻击范式。首先，无目标对抗攻击诱导MLLMs为每个物体生成错误的边界框。此外，排他性目标对抗攻击会使所有生成输出指向同一个目标边界框。另外，置换式目标对抗攻击旨在在单张图像内的不同物体之间置换所有边界框。大量实验表明，所提出的方法能够成功攻击MLLMs的视觉定位能力。我们的方法不仅为设计新型攻击提供了新视角，还可作为提升MLLMs视觉定位对抗鲁棒性的强基线。
英文摘要：
Multi-modal Large Language Models (MLLMs) have recently achieved enhanced performance across various vision-language tasks including visual grounding capabilities. However, the adversarial robustness of visual grounding remains unexplored in MLLMs. To fill this gap, we use referring expression comprehension (REC) as an example task in visual grounding and propose three adversarial attack paradigms as follows. Firstly, untargeted adversarial attacks induce MLLMs to generate incorrect bounding boxes for each object. Besides, exclusive targeted adversarial attacks cause all generated outputs to the same target bounding box. In addition, permuted targeted adversarial attacks aim to permute all bounding boxes among different objects within a single image. Extensive experiments demonstrate that the proposed methods can successfully attack visual grounding capabilities of MLLMs. Our methods not only provide a new perspective for designing novel attacks but also serve as a strong baseline for improving the adversarial robustness for visual grounding of MLLMs.
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