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English(EN) Transferable Spatial Temporal Coherence Adversarial Attack on Black-Box Vision Language Models for Autonomous Driving

新型对抗性攻击针对自动驾驶领域的视觉语言模型

研究人员开发了一种名为时空连贯性对抗性攻击(STCA)的新型对抗性攻击方法,该方法专门针对自动驾驶系统使用的黑盒视觉语言模型(VLMs)。该攻击分为三个阶段:用于语义帧选择的模态扩展,用于在保持相似性的同时创建扰动的空间攻击,以及使用运动引导掩码来破坏时间连贯性的STCA阶段。在BDD100K和nuScenes数据集上使用Video LLaVA-7B、Qwen2.5-VL-7B和Dolphin等模型进行的实验表明,当前的VLMs极易受到此类攻击,凸显了加强防御的迫切需求。 AI

影响 突出了自动驾驶领域使用的视觉语言模型中存在的关键安全漏洞,强调了对强大防御机制的需求。

排序理由 该集群包含一篇详细介绍视觉语言模型新型对抗性攻击方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新型对抗性攻击针对自动驾驶领域的视觉语言模型

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该集群包含一篇详细介绍视觉语言模型新型对抗性攻击方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Heyam Bin Jahlan Areej Alhothali Abeer Alhothali ·

    面向自动驾驶的黑盒视觉语言模型的迁移时空连贯性对抗攻击

    arXiv:2610.08331v1 Announce Type: cross Abstract: The rapid integration of Vision Language Models (VLMs) into sensitive systems introduces critical safety vulnerabilities that remain unexplored in exist studies. While adversarial attack robustness has been extensively studied for…