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: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.
By Cong Su, Jiaju Han, Xuemeng Sun, Chengyin Hu, Qike Zhang, Jiujiang Guo, Yiwei Wei, Jiahuan Long
arXiv:2510. 16923v3 Announce Type: replace-cross Abstract: Deep learning models deployed in safety critical applications like autonomous driving use simulations to test their robustness against adversarial attacks in realistic conditions.
By Mansi Phute, Matthew Hull, Haoran Wang, Alec Helbling, ShengYun Peng, Willian Lunardi, Martin Andreoni, Wenke Lee, Duen Horng Chau
arXiv:2608. 07750v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) have found successful deployment in numerous vision perception systems.
By Cong Chen, Jean-Philippe Monteuuis, Jonathan Petit
arXiv:2607. 17077v1 Announce Type: cross Abstract: Adversarial attacks against vision models like object detectors are often evaluated under limited conditions, leaving their performance under-characterized.
By Mansi Phute, Alexander Greenhalgh, Matthew Hull, Haoran Wang, Alec Helbling, ShengYun Peng, Elliott Faa, Willian Lunardi, Martin Andreoni, Wenke Lee, Duen Horng Chau
arXiv:2608.30839v1 Announce Type: new
Abstract: Thermal Infrared detection is widely used in autonomous driving, medical AI, etc., but its security has only attracted attention recently. We propose i...
By Xiaopei Zhu, Siyuan Huang, Zhanhao Hu, Jianmin Li, Jun Zhu, Xiaolin Hu
arXiv:2609.18133v1 Announce Type: new
Abstract: Visible-infrared object detectors are used for robust perception under challenging illumination and weather conditions. Current physical attacks apply...
By Yueqi Zhu, Qi Ming, Guo Cheng, Yongkang Zhang, Feiran Liu, Juan Fang, Jiahuan Zhou, Jiangmeng Li, Yuhan Zhang
arXiv:2606. 14658v1 Announce Type: cross Abstract: Artificial Intelligence (AI) is increasingly used to automate a variety of real-world computer vision (CV) applications, such as autonomous vehicle control, facial recognition, and security cameras.
By Nicole Villavicencio-Gardu\~no, Maksim Ekin Eren, Milo Prisbrey, Ben Migliori, Michael Teti
arXiv:2606. 13042v1 Announce Type: new Abstract: In intelligent video surveillance, cameras record image sequences during day and night.
By Vanessa Buhrmester, Ann-Kristin Grosselfinger, David Munch, Michael Arens
Vision Transformers (ViTs) increasingly rely on input-adaptive inference, such as token pruning and early halting, to meet energy and latency budgets. This survey examines a recent class of adversarial efficiency degradation attacks that target these mechanisms to increase computation without necessarily degrading accuracy.
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.
By Chengyin Hu, Dingyi Lu, Jiaju Han, Xiang Chen, Weiwen Shi, Jiahuan Long, Yiwei Wei, Jiujiang Guo
arXiv:2606. 00159v1 Announce Type: cross Abstract: Deep neural network (DNN)-based object detectors are widely used for analyzing aerial and satellite imagery in applications such as environmental monitoring and urban analytics.
By Jung Heum Woo, Eun-Kyu Lee