# Toward Robust and Accurate Adversarial Camouflage Generation against Vehicle Detectors

arXiv: https://arxiv.org/abs/2411.10029 · HTML: https://mlsanity.com/paper/2411.10029/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 30 references that are in the map.
- First posted on arXiv 15 Nov 2024.

## Builds on (most cited first, 22 of 22)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [CARLA: An Open Urban Driving Simulator](https://mlsanity.com/paper/1711.03938/) (2017)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Sparse R-CNN: End-to-End Object Detection with Learnable Proposals](https://mlsanity.com/paper/2011.12450/) (2020)
- [Neural 3D Mesh Renderer](https://mlsanity.com/paper/1711.07566/) (2017)
- [Understanding Deep Image Representations by Inverting Them](https://mlsanity.com/paper/1412.0035/) (2014)
- [ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector](https://mlsanity.com/paper/1804.05810/) (2018)
- [Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World](https://mlsanity.com/paper/2103.01050/) (2021)
- [Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training](https://mlsanity.com/paper/2004.06002/) (2020)
- [Local Gradients Smoothing: Defense against localized adversarial attacks](https://mlsanity.com/paper/1807.01216/) (2018)
- [FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack](https://mlsanity.com/paper/2109.07193/) (2021)
- [DTA: Physical Camouflage Attacks using Differentiable Transformation Network](https://mlsanity.com/paper/2203.09831/) (2022)
- [Physical Adversarial Attack on Vehicle Detector in the Carla Simulator](https://mlsanity.com/paper/2007.16118/) (2020)
- [Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous Driving](https://mlsanity.com/paper/2403.17301/) (2024)
- [PAD: Patch-Agnostic Defense against Adversarial Patch Attacks](https://mlsanity.com/paper/2404.16452/) (2024)
- [ACTIVE: Towards Highly Transferable 3D Physical Camouflage for Universal and Robust Vehicle Evasion](https://mlsanity.com/paper/2308.07009/) (2023)
- [RAUCA: A Novel Physical Adversarial Attack on Vehicle Detectors via Robust and Accurate Camouflage Generation](https://mlsanity.com/paper/2402.15853/) (2024)

## Built on by (most cited first, 1 of 1)

- [R-PGA: Robust Physical Adversarial Camouflage Generation via Relightable 3D Gaussian Splatting](https://mlsanity.com/paper/2603.26067/) (2026)

## Abstract (arXiv)

Adversarial camouflage is a widely used physical attack against vehicle detectors for its superiority in multi-view attack performance. One promising approach involves using differentiable neural renderers to facilitate adversarial camouflage optimization through gradient back-propagation. However, existing methods often struggle to capture environmental characteristics during the rendering process or produce adversarial textures that can precisely map to the target vehicle. Moreover, these approaches neglect diverse weather conditions, reducing the efficacy of generated camouflage across varying weather scenarios. To tackle these challenges, we propose a robust and accurate camouflage generation method, namely RAUCA. The core of RAUCA is a novel neural rendering component, End-to-End Neural Renderer Plus (E2E-NRP), which can accurately optimize and project vehicle textures and render images with environmental characteristics such as lighting and weather. In addition, we integrate a multi-weather dataset for camouflage generation, leveraging the E2E-NRP to enhance the attack robustness. Experimental results on six popular object detectors show that RAUCA-final outperforms existing methods in both simulation and real-world settings.