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InfraPatch is a white‑box, per‑instance framework that generates small grayscale patches to target infrared‑adapted vision‑language models (IR‑VLMs). The method optimizes a single‑channel patch within a 5% local‑area budget, using proxy‑guided placement and task‑adaptive objectives to induce desired behaviors in image classification, captioning, and binary visual question answering. Across ten IR‑VLM variants tested on synthetic infrared images, InfraPatch achieves targeted attack success rates ranging from 86% to 100%, revealing significant vulnerability differences among architectures and tasks.
arXiv:2610.32230v1 Announce Type: new Abstract: Modern autonomous driving systems widely adopt multi-view Bird's-Eye-View (BEV) based 3D perception models due to their superior performance. Despite t...
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.
arXiv:2607. 06485v1 Announce Type: cross Abstract: Vision-language models (VLMs) are increasingly deployed on infrared (IR) remote sensing imagery in security-critical settings, yet their adversarial robustness remains unexamined.
Vision-language models (VLMs) are increasingly deployed on infrared (IR) remote sensing imagery in security-critical settings, yet their adversarial robustness remains unexamined. We present AirflowAttack, to our knowledge the first adversarial attack for IR remote-sensing VLMs and the first to weaponize thermal-airflow turbulence as the perturbation prior.
arXiv:2606. 17711v1 Announce Type: cross Abstract: Pixel-wise adversarial patches are computationally heavy and often visually detectable, limiting utility in security-critical systems.