# CNCA: Toward Customizable and Natural Generation of Adversarial Camouflage for Vehicle Detectors

arXiv: https://arxiv.org/abs/2409.17963 · HTML: https://mlsanity.com/paper/2409.17963/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 28 references that are in the map.
- First posted on arXiv 26 Sep 2024.

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

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [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)
- [LAION-5B: An open large-scale dataset for training next generation image-text models](https://mlsanity.com/paper/2210.08402/) (2022)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [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)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [Sparse R-CNN: End-to-End Object Detection with Learnable Proposals](https://mlsanity.com/paper/2011.12450/) (2020)
- [Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World](https://mlsanity.com/paper/2103.01050/) (2021)
- [You Only Look One-level Feature](https://mlsanity.com/paper/2103.09460/) (2021)
- [Dynamic R-CNN: Towards High Quality Object Detection via Dynamic Training](https://mlsanity.com/paper/2004.06002/) (2020)
- [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)
- [Physically Realizable Natural-Looking Clothing Textures Evade Person Detectors via 3D Modeling](https://mlsanity.com/paper/2307.01778/) (2023)
- [Physical Adversarial Attack on Vehicle Detector in the Carla Simulator](https://mlsanity.com/paper/2007.16118/) (2020)
- [ACTIVE: Towards Highly Transferable 3D Physical Camouflage for Universal and Robust Vehicle Evasion](https://mlsanity.com/paper/2308.07009/) (2023)

## Built on by (most cited first, 4 of 4)

- [TACO: Adversarial Camouflage Optimization on Trucks to Fool Object Detectors](https://mlsanity.com/paper/2410.21443/) (2024)
- [AngleRoCL: Angle-Robust Concept Learning for Physically View-Invariant T2I Adversarial Patches](https://mlsanity.com/paper/2506.09538/) (2025)
- [In-the-Wild Camouflage Attack on Vehicle Detectors through Controllable Image Editing](https://mlsanity.com/paper/2603.19456/) (2026)
- [Thermal Topology Collapse: Universal Physical Patch Attacks on Infrared Vision Systems](https://mlsanity.com/paper/2603.21876/) (2026)

## Abstract (arXiv)

Prior works on physical adversarial camouflage against vehicle detectors mainly focus on the effectiveness and robustness of the attack. The current most successful methods optimize 3D vehicle texture at a pixel level. However, this results in conspicuous and attention-grabbing patterns in the generated camouflage, which humans can easily identify. To address this issue, we propose a Customizable and Natural Camouflage Attack (CNCA) method by leveraging an off-the-shelf pre-trained diffusion model. By sampling the optimal texture image from the diffusion model with a user-specific text prompt, our method can generate natural and customizable adversarial camouflage while maintaining high attack performance. With extensive experiments on the digital and physical worlds and user studies, the results demonstrate that our proposed method can generate significantly more natural-looking camouflage than the state-of-the-art baselines while achieving competitive attack performance. Our code is available at hrefhttps://anonymous.4open.science/r/CNCA-1D54https://anonymous.4open.science/r/CNCA-1D54