# Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real World

arXiv: https://arxiv.org/abs/2205.05909 · HTML: https://mlsanity.com/paper/2205.05909/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 40 references that are in the map.
- First posted on arXiv 12 May 2022.

## Builds on (most cited first, 26 of 26)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [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)
- [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)
- [Cascade R-CNN: Delving into High Quality Object Detection](https://mlsanity.com/paper/1712.00726/) (2017)
- [Adversarial Machine Learning at Scale](https://mlsanity.com/paper/1611.01236/) (2016)
- [ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models](https://mlsanity.com/paper/1708.03999/) (2017)
- [Delving into Transferable Adversarial Examples and Black-box Attacks](https://mlsanity.com/paper/1611.02770/) (2016)
- [Countering Adversarial Images using Input Transformations](https://mlsanity.com/paper/1711.00117/) (2017)
- [Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks](https://mlsanity.com/paper/1704.01155/) (2017)
- [Fooling automated surveillance cameras: adversarial patches to attack person detection](https://mlsanity.com/paper/1904.08653/) (2019)
- [Generating Adversarial Examples with Adversarial Networks](https://mlsanity.com/paper/1801.02610/) (2018)
- [Physically Realizable Adversarial Examples for LiDAR Object Detection](https://mlsanity.com/paper/2004.00543/) (2020)
- [Invisible for both Camera and LiDAR: Security of Multi-Sensor Fusion based Perception in Autonomous Driving Under Physical-World Attacks](https://mlsanity.com/paper/2106.09249/) (2021)
- [Universal Physical Camouflage Attacks on Object Detectors](https://mlsanity.com/paper/1909.04326/) (2019)
- [Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink](https://mlsanity.com/paper/2103.06504/) (2021)
- [AdvFaces: Adversarial Face Synthesis](https://mlsanity.com/paper/1908.05008/) (2019)
- [Fooling thermal infrared pedestrian detectors in real world using small bulbs](https://mlsanity.com/paper/2101.08154/) (2021)
- [Can 3D Adversarial Logos Cloak Humans?](https://mlsanity.com/paper/2006.14655/) (2020)

## Built on by (most cited first, 30 of 42)

- [Physical Adversarial Attack meets Computer Vision: A Decade Survey](https://mlsanity.com/paper/2209.15179/) (2022)
- [X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection](https://mlsanity.com/paper/2302.09491/) (2023)
- [Physically Realizable Natural-Looking Clothing Textures Evade Person Detectors via 3D Modeling](https://mlsanity.com/paper/2307.01778/) (2023)
- [Physically Adversarial Infrared Patches with Learnable Shapes and Locations](https://mlsanity.com/paper/2303.13868/) (2023)
- [HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable Design](https://mlsanity.com/paper/2212.05709/) (2022)
- [Unified Adversarial Patch for Cross-modal Attacks in the Physical World](https://mlsanity.com/paper/2307.07859/) (2023)
- [A Survey on Physical Adversarial Attack in Computer Vision](https://mlsanity.com/paper/2209.14262/) (2022)
- [SceneTAP: Scene-Coherent Typographic Adversarial Planner against Vision-Language Models in Real-World Environments](https://mlsanity.com/paper/2412.00114/) (2024)
- [Unified Adversarial Patch for Visible-Infrared Cross-modal Attacks in the Physical World](https://mlsanity.com/paper/2307.14682/) (2023)
- [Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook](https://mlsanity.com/paper/2308.06173/) (2023)
- [Physical Adversarial Attacks for Surveillance: A Survey](https://mlsanity.com/paper/2305.01074/) (2023)
- [Visual Adversarial Attacks and Defenses in the Physical World: A Survey](https://mlsanity.com/paper/2211.01671/) (2022)
- [Adversarial Infrared Blocks: A Multi-view Black-box Attack to Thermal Infrared Detectors in Physical World](https://mlsanity.com/paper/2304.10712/) (2023)
- [Adversarial Examples in the Physical World: A Survey](https://mlsanity.com/paper/2311.01473/) (2023)
- [Adversarial Infrared Curves: An Attack on Infrared Pedestrian Detectors in the Physical World](https://mlsanity.com/paper/2312.14217/) (2023)
- [Infrared Adversarial Car Stickers](https://mlsanity.com/paper/2405.09924/) (2024)
- [CBA: Contextual Background Attack against Optical Aerial Detection in the Physical World](https://mlsanity.com/paper/2302.13519/) (2023)
- [CDUPatch: Color-Driven Universal Adversarial Patch Attack for Dual-Modal Visible-Infrared Detectors](https://mlsanity.com/paper/2504.10888/) (2025)
- [A Comprehensive Study on the Robustness of Image Classification and Object Detection in Remote Sensing: Surveying and Benchmarking](https://mlsanity.com/paper/2306.12111/) (2023)
- [Real-world Adversarial Defense against Patch Attacks based on Diffusion Model](https://mlsanity.com/paper/2409.09406/) (2024)
- [Adversarial Attacks on Event-Based Pedestrian Detectors: A Physical Approach](https://mlsanity.com/paper/2503.00377/) (2025)
- [Defending Against Physical Adversarial Patch Attacks on Infrared Human Detection](https://mlsanity.com/paper/2309.15519/) (2023)
- [Two-stage optimized unified adversarial patch for attacking visible-infrared cross-modal detectors in the physical world](https://mlsanity.com/paper/2312.01789/) (2023)
- [Physics-Based Adversarial Attack on Near-Infrared Human Detector for Nighttime Surveillance Camera Systems](https://mlsanity.com/paper/2412.13709/) (2024)
- [Eigenpatches -- Adversarial Patches from Principal Components](https://mlsanity.com/paper/2306.10963/) (2023)
- [Multi-View Black-Box Physical Attacks on Infrared Pedestrian Detectors Using Adversarial Infrared Grid](https://mlsanity.com/paper/2407.01168/) (2024)
- [A Survey of Adversarial Defenses in Vision-based Systems: Categorization, Methods and Challenges](https://mlsanity.com/paper/2503.00384/) (2025)
- [Learning Fourier shapes to probe the geometric world of deep neural networks](https://mlsanity.com/paper/2511.04970/) (2025)
- [Knowledge-Guided Adversarial Training for Infrared Object Detection via Thermal Radiation Modeling](https://mlsanity.com/paper/2603.25170/) (2026)
- [Physical Adversarial Clothing Evades Visible-Thermal Detectors via Non-Overlapping RGB-T Pattern](https://mlsanity.com/paper/2605.04675/) (2026)

## Abstract (arXiv)

Thermal infrared imaging is widely used in body temperature measurement, security monitoring, and so on, but its safety research attracted attention only in recent years. We proposed the infrared adversarial clothing, which could fool infrared pedestrian detectors at different angles. We simulated the process from cloth to clothing in the digital world and then designed the adversarial "QR code" pattern. The core of our method is to design a basic pattern that can be expanded periodically, and make the pattern after random cropping and deformation still have an adversarial effect, then we can process the flat cloth with an adversarial pattern into any 3D clothes. The results showed that the optimized "QR code" pattern lowered the Average Precision (AP) of YOLOv3 by 87.7%, while the random "QR code" pattern and blank pattern lowered the AP of YOLOv3 by 57.9% and 30.1%, respectively, in the digital world. We then manufactured an adversarial shirt with a new material: aerogel. Physical-world experiments showed that the adversarial "QR code" pattern clothing lowered the AP of YOLOv3 by 64.6%, while the random "QR code" pattern clothing and fully heat-insulated clothing lowered the AP of YOLOv3 by 28.3% and 22.8%, respectively. We used the model ensemble technique to improve the attack transferability to unseen models.