English(EN) A Unified Evaluation Framework of Physical Adversarial Robustness in the Edge VLM Era

新框架评估边缘视觉语言模型的物理对抗鲁棒性

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-22 04:00

研究人员开发了一个新框架来评估边缘视觉语言模型（VLM）的物理对抗鲁棒性。该框架引入了一个名为条件攻击成功率（cASR）的指标，用于将VLM与传统目标检测器进行比较。研究发现，VLM在对抗攻击方面可能与目标检测器一样脆弱，失败模式通常取决于模型的输出格式而非其架构。值得注意的是，在分布变化下，VLM可能会转向更危险的失败模式，如位移和替换。 AI

影响这项研究突显了边缘VLM潜在的安全漏洞，这对于在安全关键型应用中部署这些模型的开发人员至关重要。

排序理由介绍VLM鲁棒性新评估框架和指标的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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arXiv cs.LG TIER_1 English(EN) · Eric Liang, Alan Luo, Kaiwen Yuan · 2026-10-22 04:00

A Unified Evaluation Framework of Physical Adversarial Robustness in the Edge VLM Era

arXiv:2610.29196v1 Announce Type: cross Abstract: Recent advancements in Vision-Language Models (VLMs) enable end-to-end visual grounding via language prompts and generative decoding, demonstrating largely improved zero-shot and open-vocabulary detection capabilities. As a result…

本文如何被排名

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A Unified Evaluation Framework of Physical Adversarial Robustness in the Edge VLM Era

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