Physical Adversarial Textures that Fool Visual Object Tracking
We present a system for generating inconspicuous-looking textures that, when displayed in the physical world as digital or printed posters, cause visual object tracking systems to become confused. For instance, as a target being tracked by a robot's camera moves in front of such a poster, our generated texture makes the tracker lock onto it and allows the target to evade. This work aims to fool seldom-targeted regression tasks, and in particular compares diverse optimization strategies: non-targeted, targeted, and a new family of guided adversarial losses. While we use the Expectation Over Transformation (EOT) algorithm to generate physical adversaries that fool tracking models when imaged under diverse conditions, we compare the impacts of different conditioning variables, including viewpoint, lighting, and appearances, to find practical attack setups with high resulting adversarial strength and convergence speed. We further showcase textures optimized solely using simulated scenes can confuse real-world tracking systems.
Code (0)
등록된 구현이 없습니다.
Tasks
ObjectObject TrackingVisual Object TrackingSimilar Papers 제목 키워드 기반
Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous Driving
Deep learning-based monocular depth estimation (MDE), extensively applied in autonomous driving, is known to be vulnerable to adversarial attacks. Previous physical attacks against MDE models rely on 2D adversarial patch…
Adversarial AttackAutonomous DrivingDepth EstimationMonocular Depth EstimationTACO: Adversarial Camouflage Optimization on Trucks to Fool Object Detectors
Adversarial attacks threaten the reliability of machine learning models in critical applications like autonomous vehicles and defense systems. As object detectors become more robust with models like YOLOv8, developing ef…
Autonomous VehiclesObjectobject-detectionObject DetectionAdversarial Attacks on Event-Based Pedestrian Detectors: A Physical Approach
Event cameras, known for their low latency and high dynamic range, show great potential in pedestrian detection applications. However, while recent research has primarily focused on improving detection accuracy, the robu…
Pedestrian DetectionPhysical Adversarial Examples for Object Detectors
Deep neural networks (DNNs) are vulnerable to adversarial examples-maliciously crafted inputs that cause DNNs to make incorrect predictions. Recent work has shown that these attacks generalize to the physical domain, to …
Objectobject-detectionObject DetectionTex3D: Objects as Attack Surfaces via Adversarial 3D Textures for Vision-Language-Action Models
Vision-language-action (VLA) models have shown strong performance in robotic manipulation, yet their robustness to physically realizable adversarial attacks remains underexplored. Existing studies reveal vulnerabilities …