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arXiv Machine Learning By Eric Liang, Alan Luo, Kaiwen Yuan

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

Read the original on arXiv Machine Learning →

The paper introduces a unified evaluation framework for assessing the physical adversarial robustness of Vision‑Language Models (VLMs) deployed at the edge. It defines a conditional attack success rate (cASR) metric that counts only images where the model still localizes objects under a gray occluder, and categorizes failures into omission, substitution, and displacement. Benchmarking shows that compact grounding VLMs can be as vulnerable as traditional object detectors, with omission being the only failure visible to downstream systems, while displacement and substitution pose potentially more dangerous risks, especially under distribution shift.

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