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Physical Adversarial Attack on Vehicle Detector in the Carla Simulator

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arxiv 2007.16118 v2 pith:ZMMUXLTM submitted 2020-07-31 cs.CV

Tong Wu , Xuefei Ning , Wenshuo Li , Ranran Huang , Huazhong Yang , Yu Wang This is my paper · ORCID
classification cs.CV
keywords adversarialsimulatorvehicleapproachcarladetectordetectorsphysical
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In this paper, we tackle the issue of physical adversarial examples for object detectors in the wild. Specifically, we proposed to generate adversarial patterns to be applied on vehicle surface so that it's not recognizable by detectors in the photo-realistic Carla simulator. Our approach contains two main techniques, an Enlarge-and-Repeat process and a Discrete Searching method, to craft mosaic-like adversarial vehicle textures without access to neither the model weight of the detector nor a differential rendering procedure. The experimental results demonstrate the effectiveness of our approach in the simulator.

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Forward citations

Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. 3D Gaussian Splatting Driven Multi-View Robust Physical Adversarial Camouflage Generation

    cs.CV 2025-07 conditional novelty 7.0 of 10

    PGA uses 3D Gaussian Splatting to generate physical adversarial camouflage from a few images, improving multi-view attack robustness on vehicle detectors.

  2. ALLUDE: A Unified Evaluation System for Configurable Attacks in Differentiable Environments

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A configurable, cross-platform simulator-based evaluation platform shows that object class and camera trajectory, not weather or detector choice, dominate whether 3D adversarial patch attacks succeed, and that all tes...

  3. UNDREAM: Bridging Differentiable Rendering and Photorealistic Simulation for End-to-end Adversarial Attacks

    cs.CR 2025-10 conditional novelty 6.0 of 10

    UnDREAM enables optimization of adversarial textures on arbitrary 3D objects inside Unreal Engine by bridging the simulator to the differentiable renderer Mitsuba.

  4. Kaleidoscopic Background Attack: Disrupting Pose Estimation with Multi-Fold Radial Symmetry Textures

    cs.CV 2025-07 conditional novelty 6.0 of 10

    A radially symmetric 'kaleidoscope' background disc, optimized with a projected orientation consistency loss, disrupts sparse-view camera pose estimation across six models in both digital and physical experiments.

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