# ACTIVE: Towards Highly Transferable 3D Physical Camouflage for Universal and Robust Vehicle Evasion

arXiv: https://arxiv.org/abs/2308.07009 · HTML: https://mlsanity.com/paper/2308.07009/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 50 references that are in the map.
- First posted on arXiv 14 Aug 2023.

## Builds on (most cited first, 30 of 36)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [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)
- [Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions](https://mlsanity.com/paper/2102.12122/) (2021)
- [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)
- [EfficientDet: Scalable and Efficient Object Detection](https://mlsanity.com/paper/1911.09070/) (2019)
- [YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors](https://mlsanity.com/paper/2207.02696/) (2022)
- [Adversarial Patch](https://mlsanity.com/paper/1712.09665/) (2017)
- [Sparse R-CNN: End-to-End Object Detection with Learnable Proposals](https://mlsanity.com/paper/2011.12450/) (2020)
- [Threat of Adversarial Attacks on Deep Learning in Computer Vision: A Survey](https://mlsanity.com/paper/1801.00553/) (2018)
- [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)
- [MaX-DeepLab: End-to-End Panoptic Segmentation with Mask Transformers](https://mlsanity.com/paper/2012.00759/) (2020)
- [Axial-DeepLab: Stand-Alone Axial-Attention for Panoptic Segmentation](https://mlsanity.com/paper/2003.07853/) (2020)
- [Fooling automated surveillance cameras: adversarial patches to attack person detection](https://mlsanity.com/paper/1904.08653/) (2019)
- [Speed/accuracy trade-offs for modern convolutional object detectors](https://mlsanity.com/paper/1611.10012/) (2016)
- [On the Robustness of Vision Transformers to Adversarial Examples](https://mlsanity.com/paper/2104.02610/) (2021)
- [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)
- [FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack](https://mlsanity.com/paper/2109.07193/) (2021)

## Built on by (most cited first, 30 of 31)

- [Physical 3D Adversarial Attacks against Monocular Depth Estimation in Autonomous Driving](https://mlsanity.com/paper/2403.17301/) (2024)
- [Towards Transferable Targeted 3D Adversarial Attack in the Physical World](https://mlsanity.com/paper/2312.09558/) (2023)
- [RAUCA: A Novel Physical Adversarial Attack on Vehicle Detectors via Robust and Accurate Camouflage Generation](https://mlsanity.com/paper/2402.15853/) (2024)
- [Adversarial Examples in the Physical World: A Survey](https://mlsanity.com/paper/2311.01473/) (2023)
- [Safety of Embodied Navigation: A Survey](https://mlsanity.com/paper/2508.05855/) (2025)
- [Rethinking Impersonation and Dodging Attacks on Face Recognition Systems](https://mlsanity.com/paper/2401.08903/) (2024)
- [Flexible Physical Camouflage Generation Based on a Differential Approach](https://mlsanity.com/paper/2402.13575/) (2024)
- [3D Gaussian Splatting Driven Multi-View Robust Physical Adversarial Camouflage Generation](https://mlsanity.com/paper/2507.01367/) (2025)
- [Towards Physically Realizable Adversarial Attacks in Embodied Vision Navigation](https://mlsanity.com/paper/2409.10071/) (2024)
- [Embodied Laser Attack:Leveraging Scene Priors to Achieve Agent-based Robust Non-contact Attacks](https://mlsanity.com/paper/2312.09554/) (2023)
- [Adversarial Attacks on Both Face Recognition and Face Anti-spoofing Models](https://mlsanity.com/paper/2405.16940/) (2024)
- [CNCA: Toward Customizable and Natural Generation of Adversarial Camouflage for Vehicle Detectors](https://mlsanity.com/paper/2409.17963/) (2024)
- [Improving the Transferability of Adversarial Attacks on Face Recognition with Diverse Parameters Augmentation](https://mlsanity.com/paper/2411.15555/) (2024)
- [Attack Anything: Blind DNNs via Universal Background Adversarial Attack](https://mlsanity.com/paper/2409.00029/) (2024)
- [RenderBender: A Survey on Adversarial Attacks Using Differentiable Rendering](https://mlsanity.com/paper/2411.09749/) (2024)
- [Physical Adversarial Camouflage through Gradient Calibration and Regularization](https://mlsanity.com/paper/2508.05414/) (2025)
- [Disrupting Vision-Language Model-Driven Navigation Services via Adversarial Object Fusion](https://mlsanity.com/paper/2505.23266/) (2025)
- [3DGAA: Realistic and Robust 3D Gaussian-based Adversarial Attack for Autonomous Driving](https://mlsanity.com/paper/2507.09993/) (2025)
- [Toward Robust and Accurate Adversarial Camouflage Generation against Vehicle Detectors](https://mlsanity.com/paper/2411.10029/) (2024)
- [SABER: Spatially Consistent 3D Universal Adversarial Objects for BEV Detectors](https://mlsanity.com/paper/2505.22499/) (2025)
- [Transferable Physical-World Adversarial Patches Against Object Detection in Autonomous Driving](https://mlsanity.com/paper/2604.23105/) (2026)
- [PADetBench: Towards Benchmarking Physical Attacks against Object Detection](https://mlsanity.com/paper/2408.09181/) (2024)
- [Adversarial Manhole: Challenging Monocular Depth Estimation and Semantic Segmentation Models with Patch Attack](https://mlsanity.com/paper/2408.14879/) (2024)
- [TACO: Adversarial Camouflage Optimization on Trucks to Fool Object Detectors](https://mlsanity.com/paper/2410.21443/) (2024)
- [Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection](https://mlsanity.com/paper/2412.16358/) (2024)
- [ComplicitSplat: Downstream Models are Vulnerable to Blackbox Attacks by 3D Gaussian Splat Camouflages](https://mlsanity.com/paper/2508.11854/) (2025)
- [OBJVanish: Physically Realizable Text-to-3D Adv. Generation of LiDAR-Invisible Objects](https://mlsanity.com/paper/2510.06952/) (2025)
- [R-PGA: Robust Physical Adversarial Camouflage Generation via Relightable 3D Gaussian Splatting](https://mlsanity.com/paper/2603.26067/) (2026)
- [Still Camouflage, Moving Illusion: View-Induced Trajectory Manipulation in Autonomous Driving](https://mlsanity.com/paper/2605.12743/) (2026)
- [Towards Universal Physical Adversarial Attacks via a Joint Multi-Objective and Multi-Model Optimization Framework](https://mlsanity.com/paper/2605.17772/) (2026)

## Abstract (arXiv)

Adversarial camouflage has garnered attention for its ability to attack object detectors from any viewpoint by covering the entire object's surface. However, universality and robustness in existing methods often fall short as the transferability aspect is often overlooked, thus restricting their application only to a specific target with limited performance. To address these challenges, we present Adversarial Camouflage for Transferable and Intensive Vehicle Evasion (ACTIVE), a state-of-the-art physical camouflage attack framework designed to generate universal and robust adversarial camouflage capable of concealing any 3D vehicle from detectors. Our framework incorporates innovative techniques to enhance universality and robustness, including a refined texture rendering that enables common texture application to different vehicles without being constrained to a specific texture map, a novel stealth loss that renders the vehicle undetectable, and a smooth and camouflage loss to enhance the naturalness of the adversarial camouflage. Our extensive experiments on 15 different models show that ACTIVE consistently outperforms existing works on various public detectors, including the latest YOLOv7. Notably, our universality evaluations reveal promising transferability to other vehicle classes, tasks (segmentation models), and the real world, not just other vehicles.