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.
arXiv:2608.28778v1 Announce Type: cross Abstract: Autonomous vehicles (AVs) rely on accurate camera-LiDAR calibration for multimodal sensor fusion. In practice, calibration can drift due to vibration...
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.
arXiv:2606.14504v2 Announce Type: replace Abstract: Physical adversarial attacks on vision systems are typically studied via scene manipulation, such as adversarial patches or projections. A smaller...
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...
LiDAR-SHIELD is a fully Dockerized framework that provides a unified interface for testing LiDAR-based 3D perception models against adversarial attacks. It exposes any model through three lightweight HTTP endpoints (/info, /forward, /gradient), allowing easy integration of new models or datasets with a single loader function. The framework supports both 3D semantic segmentation and 3D object detection, incorporating state‑of‑the‑art attacks such as PGD, Square Attack, point perturbation, point removal, point injection, and LiDAttack to systematically reveal vulnerabilities across diverse LiDAR backbones.
DriveWorld3D is a framework that integrates temporal video learning, multi‑sensor measurement constraints, and offline generative simulation to build a unified 3D world model for autonomous driving. Its online component tracks road surfaces, volumetric occupancy, dynamic entities, and road relations while explicitly managing physical occupancy, observability, and evidence provenance. The offline component reconstructs scenes with neural radiance fields or 3D Gaussian Splatting and expands rare conditions using generative models, but limits generated content from being used as geometric ground truth.
Camera-based object detectors are vulnerable to physical adversarial attacks designed to suppress detections. While adversarial training and input purification offer some protection, they often overfit to specific attack distributions and fail on adaptive adversaries.
By leveraging data from video-based perception systems, intelligent transportation systems (ITS) support safety-critical applications that improve road safety. However, adversaries may manipulate video frames to compromise downstream perception modules, causing failures in safety-critical functions and increasing risks to vulnerable road users.
arXiv:2608. 16031v1 Announce Type: new Abstract: Camera-based object detectors are vulnerable to physical adversarial attacks designed to suppress detections.
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...
arXiv:2607. 11560v1 Announce Type: cross Abstract: Vision-language agents (VLAs) are increasingly used to interpret complex driving scenes and support safety-critical reasoning.
arXiv:2608.20748v1 Announce Type: new Abstract: The Visual Geometry Grounded Transformer (VGGT) enables unified feed-forward 3D reconstruction from multi-view images. However, deploying such a high-p...