The paper presents 3DGAA, a fabrication-first framework that generates view-consistent, geometry-preserving adversarial wraps for vehicles using 3D Gaussian splatting optimization. It ensures consistency across viewpoints, illumination, and occlusion while limiting changes to vehicle geometry, producing realistic print-only textures that significantly reduce detection confidence and average precision in simulations and physical tests. Ablation and efficiency studies analyze the impact of physical filtering, augmentation, and shape-consistency regularization, and the method demonstrates robustness against common preprocessing defenses and cross-detector transferability.
By Yixun Zhang, Lizhi Wang, Junjun Zhao, Wending Zhao, Feng Zhou, Yonghao Dang, Jianqin Yin
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 ex...
By Heyam Bin Jahlan Areej Alhothali Abeer Alhothali
arXiv:2610.15343v1 Announce Type: cross
Abstract: World models and video understanding models are increasingly deployed in dynamic visual environments, yet their robustness to physical adversarial th...
By Song Xia, Meiwen Ding, Chenqi Kong, Wenhan Yang, Xudong Jiang
arXiv:2608.29881v1 Announce Type: new
Abstract: Monocular depth estimation has achieved strong open-domain generalization, yet reliable robotic deployment remains difficult in transparent, reflective...
By Muxin Liu, Tianbo Liu, Jing Xia, Xiaoyang Lyu, Xiaoshan Wu, Bo Wang, Peng Dai, Zhongrui Wang, Shaoshuai Shi, Xiaojuan Qi
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
ComplicitSplat is a novel black‑box attack that leverages 3D Gaussian Splatting (3DGS) shading to create viewpoint‑specific camouflage, embedding adversarial content into scene objects that is only visible from certain angles. The method does not require access to model architecture or weights and can successfully fool a range of popular object detectors—including single‑stage, multi‑stage, and transformer‑based models—on both real‑world physical objects and synthetic scenes. This demonstrates that downstream models using 3DGS are vulnerable to adversarial manipulation.
By Matthew Hull, Haoyang Yang, Pratham Mehta, Mansi Phute, Aeree Cho, Haorang Wang, Matthew Lau, Wenke Lee, Wilian Lunardi, Martin Andreoni, Duen Horng Chau
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:2607. 05516v2 Announce Type: replace-cross Abstract: Model-specific adversarial attacks have been extensively studied.
By Paul K. Mandal, Pavan Reddy, Tristan Malatynski
arXiv:2607. 05516v1 Announce Type: cross Abstract: Model-specific adversarial attacks have been extensively studied.
By Paul K. Mandal, Pavan Reddy, Tristan Malatynski
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
The paper introduces MIRA-Bench, a mirror‑centric RGB‑D dataset and benchmark designed to test vision‑language models (VLMs) in reflective scenes where depth information is misleading. It contains 1,011 RGB‑D images from 525 real‑world scenes, annotated with reflective surface masks and object labels for both direct and reflected views, and 14,140 QA pairs across eight sub‑tasks. Evaluation shows that state‑of‑the‑art VLMs consistently fail to distinguish real from reflected objects, misalign reflected instances with their real counterparts, and produce incorrect spatial inferences, with depth data even degrading performance.
whyItMatters":"The study reveals that current VLMs cannot handle mirror‑induced depth ambiguity, highlighting a critical limitation in spatial reasoning for real‑world applications involving reflective surfaces."
By Hao Yin, Tianchen Guo, Heming Du, Yan Ke, Yanbin Liu, Xin Yu
arXiv:2511. 20853v4 Announce Type: replace-cross Abstract: Training and evaluation of state-of-the-art computer vision algorithms for reliable shallow depth of field (DoF) rendering and defocus deblurring remain constrained by a persistent lack of large-scale, full-frame, high fidelity, real-image datasets.
By Nisarg K. Trivedi, Vinayaka A. Belludi, Li-Yun Wang