# Adversarial Infrared Curves: An Attack on Infrared Pedestrian Detectors in the Physical World

arXiv: https://arxiv.org/abs/2312.14217 · HTML: https://mlsanity.com/paper/2312.14217/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 33 of its 46 references that are in the map.
- First posted on arXiv 21 Dec 2023.

## Builds on (most cited first, 30 of 33)

- [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)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Understanding Deep Image Representations by Inverting Them](https://mlsanity.com/paper/1412.0035/) (2014)
- [Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detection](https://mlsanity.com/paper/2203.16220/) (2022)
- [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)
- [Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectors](https://mlsanity.com/paper/1910.14667/) (2019)
- [Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World](https://mlsanity.com/paper/2103.01050/) (2021)
- [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)
- [Adversarial Sticker: A Stealthy Attack Method in the Physical World](https://mlsanity.com/paper/2104.06728/) (2021)
- [Universal Physical Camouflage Attacks on Object Detectors](https://mlsanity.com/paper/1909.04326/) (2019)
- [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)
- [SegPGD: An Effective and Efficient Adversarial Attack for Evaluating and Boosting Segmentation Robustness](https://mlsanity.com/paper/2207.12391/) (2022)
- [Adversarial camera stickers: A physical camera-based attack on deep learning systems](https://mlsanity.com/paper/1904.00759/) (2019)
- [The Translucent Patch: A Physical and Universal Attack on Object Detectors](https://mlsanity.com/paper/2012.12528/) (2020)
- [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)
- [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)
- [Query-Efficient Black-box Adversarial Attacks Guided by a Transfer-based Prior](https://mlsanity.com/paper/2203.06560/) (2022)
- [TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systems](https://mlsanity.com/paper/2111.09999/) (2021)
- [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)

## Built on by (most cited first, 10 of 10)

- [Thermally Activated Dual-Modal Adversarial Clothing against AI Surveillance Systems](https://mlsanity.com/paper/2511.09829/) (2025)
- [Multi-View Black-Box Physical Attacks on Infrared Pedestrian Detectors Using Adversarial Infrared Grid](https://mlsanity.com/paper/2407.01168/) (2024)
- [Thermal Topology Collapse: Universal Physical Patch Attacks on Infrared Vision Systems](https://mlsanity.com/paper/2603.21876/) (2026)
- [Revealing Physical-World Semantic Vulnerabilities: Universal Adversarial Patch for Infrared Vision-Language Models](https://mlsanity.com/paper/2604.03117/) (2026)
- [Physical Adversarial Attacks on AI Surveillance Systems:Detection, Tracking, and Visible--Infrared Evasion](https://mlsanity.com/paper/2604.06865/) (2026)
- [AirflowAttack: Thermal-Airflow Adversarial Perturbations against Infrared Remote-Sensing Vision-Language Models](https://mlsanity.com/paper/2607.06485/) (2026)
- [InfraQR: Edge-Placed QR-Inspired Structured Patch Attacks on Infrared Vision-Language Models](https://mlsanity.com/paper/2607.07288/) (2026)
- [QR-Structured Thermal Triggers for Targeted Semantic Attacks on Infrared Vision-Language Models](https://mlsanity.com/paper/2607.29445/) (2026)
- [Physical Adversarial Examples for Person Detectors in Thermal Images Based on 3D Modeling](https://mlsanity.com/paper/2608.30839/) (2026)
- [InfraPatch: Cross-Task Targeted Grayscale Patch Attacks on Infrared-Adapted Vision-Language Models](https://mlsanity.com/paper/2609.02233/) (2026)

## Abstract (arXiv)

Deep neural network security is a persistent concern, with considerable research on visible light physical attacks but limited exploration in the infrared domain. Existing approaches, like white-box infrared attacks using bulb boards and QR suits, lack realism and stealthiness. Meanwhile, black-box methods with cold and hot patches often struggle to ensure robustness. To bridge these gaps, we propose Adversarial Infrared Curves (AdvIC). Using Particle Swarm Optimization, we optimize two Bezier curves and employ cold patches in the physical realm to introduce perturbations, creating infrared curve patterns for physical sample generation. Our extensive experiments confirm AdvIC's effectiveness, achieving 94.8% and 67.2% attack success rates for digital and physical attacks, respectively. Stealthiness is demonstrated through a comparative analysis, and robustness assessments reveal AdvIC's superiority over baseline methods. When deployed against diverse advanced detectors, AdvIC achieves an average attack success rate of 76.8%, emphasizing its robust nature. we explore adversarial defense strategies against AdvIC and examine its impact under various defense mechanisms. Given AdvIC's substantial security implications for real-world vision-based applications, urgent attention and mitigation efforts are warranted.