# Adversarial Infrared Geometry: Using Geometry to Perform Adversarial Attack against Infrared Pedestrian Detectors

arXiv: https://arxiv.org/abs/2403.03674 · HTML: https://mlsanity.com/paper/2403.03674/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 40 references that are in the map.
- First posted on arXiv 6 Mar 2024.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [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)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Robust Speech Recognition via Large-Scale Weak Supervision](https://mlsanity.com/paper/2212.04356/) (2022)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models](https://mlsanity.com/paper/1708.03999/) (2017)
- [Libra R-CNN: Towards Balanced Learning for Object Detection](https://mlsanity.com/paper/1904.02701/) (2019)
- [AdvHat: Real-world adversarial attack on ArcFace Face ID system](https://mlsanity.com/paper/1908.08705/) (2019)
- [Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World](https://mlsanity.com/paper/2103.01050/) (2021)
- [Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition](https://mlsanity.com/paper/2105.03162/) (2021)
- [Adversarial Texture for Fooling Person Detectors in the Physical World](https://mlsanity.com/paper/2203.03373/) (2022)
- [A General Framework for Adversarial Examples with Objectives](https://mlsanity.com/paper/1801.00349/) (2017)
- [Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blink](https://mlsanity.com/paper/2103.06504/) (2021)
- [FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack](https://mlsanity.com/paper/2109.07193/) (2021)
- [AdvDrop: Adversarial Attack to DNNs by Dropping Information](https://mlsanity.com/paper/2108.09034/) (2021)
- [Fooling thermal infrared pedestrian detectors in real world using small bulbs](https://mlsanity.com/paper/2101.08154/) (2021)
- [Optical Adversarial Attack](https://mlsanity.com/paper/2108.06247/) (2021)
- [Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real World](https://mlsanity.com/paper/2205.05909/) (2022)
- [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)
- [Robust Physical-World Attacks on Face Recognition](https://mlsanity.com/paper/2109.09320/) (2021)
- [Adversarial Laser Spot: Robust and Covert Physical-World Attack to DNNs](https://mlsanity.com/paper/2206.01034/) (2022)
- [Adversarial Neon Beam: A Light-based Physical Attack to DNNs](https://mlsanity.com/paper/2204.00853/) (2022)
- [Adversarial Catoptric Light: An Effective, Stealthy and Robust Physical-World Attack to DNNs](https://mlsanity.com/paper/2209.11739/) (2022)

## Abstract (arXiv)

Currently, infrared imaging technology enjoys widespread usage, with infrared object detection technology experiencing a surge in prominence. While previous studies have delved into physical attacks on infrared object detectors, the implementation of these techniques remains complex. For instance, some approaches entail the use of bulb boards or infrared QR suits as perturbations to execute attacks, which entail costly optimization and cumbersome deployment processes. Other methodologies involve the utilization of irregular aerogel as physical perturbations for infrared attacks, albeit at the expense of optimization expenses and perceptibility issues. In this study, we propose a novel infrared physical attack termed Adversarial Infrared Geometry (AdvIG), which facilitates efficient black-box query attacks by modeling diverse geometric shapes (lines, triangles, ellipses) and optimizing their physical parameters using Particle Swarm Optimization (PSO). Extensive experiments are conducted to evaluate the effectiveness, stealthiness, and robustness of AdvIG. In digital attack experiments, line, triangle, and ellipse patterns achieve attack success rates of 93.1%, 86.8%, and 100.0%, respectively, with average query times of 71.7, 113.1, and 2.57, respectively, thereby confirming the efficiency of AdvIG. Physical attack experiments are conducted to assess the attack success rate of AdvIG at different distances. On average, the line, triangle, and ellipse achieve attack success rates of 61.1%, 61.2%, and 96.2%, respectively. Further experiments are conducted to comprehensively analyze AdvIG, including ablation experiments, transfer attack experiments, and adversarial defense mechanisms. Given the superior performance of our method as a simple and efficient black-box adversarial attack in both digital and physical environments, we advocate for widespread attention to AdvIG.