# Physically Realizable Natural-Looking Clothing Textures Evade Person Detectors via 3D Modeling

arXiv: https://arxiv.org/abs/2307.01778 · HTML: https://mlsanity.com/paper/2307.01778/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 51 references that are in the map.
- First posted on arXiv 4 Jul 2023.

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

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (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)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://mlsanity.com/paper/2010.04159/) (2020)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Categorical Reparameterization with Gumbel-Softmax](https://mlsanity.com/paper/1611.01144/) (2016)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Adversarial examples in the physical world](https://mlsanity.com/paper/1607.02533/) (2016)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [DeepFool: a simple and accurate method to fool deep neural networks](https://mlsanity.com/paper/1511.04599/) (2015)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images](https://mlsanity.com/paper/1412.1897/) (2014)
- [Adversarial Patch](https://mlsanity.com/paper/1712.09665/) (2017)
- [Fooling automated surveillance cameras: adversarial patches to attack person detection](https://mlsanity.com/paper/1904.08653/) (2019)
- [Physical Adversarial Examples for Object Detectors](https://mlsanity.com/paper/1807.07769/) (2018)
- [ShapeShifter: Robust Physical Adversarial Attack on Faster R-CNN Object Detector](https://mlsanity.com/paper/1804.05810/) (2018)
- [Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors](https://mlsanity.com/paper/1910.14667/) (2019)
- [A* Sampling](https://mlsanity.com/paper/1411.0030/) (2014)
- [Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World](https://mlsanity.com/paper/2103.01050/) (2021)
- [Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles](https://mlsanity.com/paper/2003.08757/) (2020)
- [Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenon](https://mlsanity.com/paper/2203.03818/) (2022)
- [Adversarial Texture for Fooling Person Detectors in the Physical World](https://mlsanity.com/paper/2203.03373/) (2022)
- [On Physical Adversarial Patches for Object Detection](https://mlsanity.com/paper/1906.11897/) (2019)
- [Universal Physical Camouflage Attacks on Object Detectors](https://mlsanity.com/paper/1909.04326/) (2019)

## Built on by (most cited first, 30 of 41)

- [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)
- [UV-Attack: Physical-World Adversarial Attacks for Person Detection via Dynamic-NeRF-based UV Mapping](https://mlsanity.com/paper/2501.05783/) (2025)
- [Infrared Adversarial Car Stickers](https://mlsanity.com/paper/2405.09924/) (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)
- [I Don't Know You, But I Can Catch You: Real-Time Defense against Diverse Adversarial Patches for Object Detectors](https://mlsanity.com/paper/2406.10285/) (2024)
- [Adversarial Attacks on Event-Based Pedestrian Detectors: A Physical Approach](https://mlsanity.com/paper/2503.00377/) (2025)
- [CNCA: Toward Customizable and Natural Generation of Adversarial Camouflage for Vehicle Detectors](https://mlsanity.com/paper/2409.17963/) (2024)
- [DynamicPAE: Generating Scene-Aware Physical Adversarial Examples in Real-Time](https://mlsanity.com/paper/2412.08053/) (2024)
- [AdvReal: Physical Adversarial Patch Generation Framework for Security Evaluation of Object Detection Systems](https://mlsanity.com/paper/2505.16402/) (2025)
- [Beyond Vulnerabilities: A Survey of Adversarial Attacks as Both Threats and Defenses in Computer Vision Systems](https://mlsanity.com/paper/2508.01845/) (2025)
- [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)
- [Adversarial Attacks on Hyperbolic Networks](https://mlsanity.com/paper/2412.01495/) (2024)
- [PBCAT: Patch-based composite adversarial training against physically realizable attacks on object detection](https://mlsanity.com/paper/2506.23581/) (2025)
- [Rethinking the Threat and Accessibility of Adversarial Attacks against Face Recognition Systems](https://mlsanity.com/paper/2407.08514/) (2024)
- [Thermally Activated Dual-Modal Adversarial Clothing against AI Surveillance Systems](https://mlsanity.com/paper/2511.09829/) (2025)
- [Transferable Physical-World Adversarial Patches Against Pedestrian Detection Models](https://mlsanity.com/paper/2604.22552/) (2026)
- [Generative Adversarial Patches for Physical Attacks on Cross-Modal Pedestrian Re-Identification](https://mlsanity.com/paper/2410.20097/) (2024)
- [Prompt-Guided Environmentally Consistent Adversarial Patch](https://mlsanity.com/paper/2411.10498/) (2024)
- [Reinforced Embodied Active Defense: Exploiting Adaptive Interaction for Robust Visual Perception in Adversarial 3D Environments](https://mlsanity.com/paper/2507.18484/) (2025)
- [UNDREAM: Bridging Differentiable Rendering and Photorealistic Simulation for End-to-end Adversarial Attacks](https://mlsanity.com/paper/2510.16923/) (2025)
- [A Single Set of Adversarial Clothes Breaks Multiple Defense Methods in the Physical World](https://mlsanity.com/paper/2510.17322/) (2025)
- [Physical Adversarial Clothing Evades Visible-Thermal Detectors via Non-Overlapping RGB-T Pattern](https://mlsanity.com/paper/2605.04675/) (2026)
- [Trainwreck: A damaging adversarial attack on image classifiers](https://mlsanity.com/paper/2311.14772/) (2023)
- [PADetBench: Towards Benchmarking Physical Attacks against Object Detection](https://mlsanity.com/paper/2408.09181/) (2024)
- [Towards an End-to-End (E2E) Adversarial Learning and Application in the Physical World](https://mlsanity.com/paper/2501.08258/) (2025)
- [AngleRoCL: Angle-Robust Concept Learning for Physically View-Invariant T2I Adversarial Patches](https://mlsanity.com/paper/2506.09538/) (2025)

## Abstract (arXiv)

Recent works have proposed to craft adversarial clothes for evading person detectors, while they are either only effective at limited viewing angles or very conspicuous to humans. We aim to craft adversarial texture for clothes based on 3D modeling, an idea that has been used to craft rigid adversarial objects such as a 3D-printed turtle. Unlike rigid objects, humans and clothes are non-rigid, leading to difficulties in physical realization. In order to craft natural-looking adversarial clothes that can evade person detectors at multiple viewing angles, we propose adversarial camouflage textures (AdvCaT) that resemble one kind of the typical textures of daily clothes, camouflage textures. We leverage the Voronoi diagram and Gumbel-softmax trick to parameterize the camouflage textures and optimize the parameters via 3D modeling. Moreover, we propose an efficient augmentation pipeline on 3D meshes combining topologically plausible projection (TopoProj) and Thin Plate Spline (TPS) to narrow the gap between digital and real-world objects. We printed the developed 3D texture pieces on fabric materials and tailored them into T-shirts and trousers. Experiments show high attack success rates of these clothes against multiple detectors.