Adversarial Infrared Blocks: A Multi-view Black-box Attack to Thermal Infrared Detectors in Physical World
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Abstract (arXiv)
Infrared imaging systems have a vast array of potential applications in pedestrian detection and autonomous driving, and their safety performance is of great concern. However, few studies have explored the safety of infrared imaging systems in real-world settings. Previous research has used physical perturbations such as small bulbs and thermal "QR codes" to attack infrared imaging detectors, but such methods are highly visible and lack stealthiness. Other researchers have used hot and cold blocks to deceive infrared imaging detectors, but this method is limited in its ability to execute attacks from various angles. To address these shortcomings, we propose a novel physical attack called adversarial infrared blocks (AdvIB). By optimizing the physical parameters of the adversarial infrared blocks, this method can execute a stealthy black-box attack on thermal imaging system from various angles. We evaluate the proposed method based on its effectiveness, stealthiness, and robustness. Our physical tests show that the proposed method achieves a success rate of over 80% under most distance and angle conditions, validating its effectiveness. For stealthiness, our method involves attaching the adversarial infrared block to the inside of clothing, enhancing its stealthiness. Additionally, we test the proposed method on advanced detectors, and experimental results demonstrate an average attack success rate of 51.2%, proving its robustness. Overall, our proposed AdvIB method offers a promising avenue for conducting stealthy, effective and robust black-box attacks on thermal imaging system, with potential implications for real-world safety and security applications.
Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 34 of its 49 references that are in the map. First posted on arXiv 21 Apr 2023.
Builds on
34 of 49 references.
References · 34
- 2015Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networkscited by 9,639
- 2020End-to-End Object Detection with Transformerscited by 7,414
- 2017Mask R-CNNcited by 7,277
- 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
- 2013Intriguing properties of neural networkscited by 5,155
- 2018YOLOv3: An Incremental Improvementcited by 2,659
- 2018Improving Transferability of Adversarial Examples with Input Diversitycited by 599
- 2017One pixel attack for fooling deep neural networkscited by 582
- 2021Recent Advances in Adversarial Training for Adversarial Robustnesscited by 279
- 2019Fooling automated surveillance cameras: adversarial patches to attack person detectioncited by 268
- 2019Libra R-CNN: Towards Balanced Learning for Object Detectioncited by 255
- 2020Adversarial Camouflage: Hiding Physical-World Attacks with Natural Stylescited by 126
22 more
- 2022Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenoncited by 101
- 2022Adversarial Texture for Fooling Person Detectors in the Physical Worldcited by 96
- 2021Adversarial Sticker: A Stealthy Attack Method in the Physical Worldcited by 93
- 2019ColorFool: Semantic Adversarial Colorizationcited by 89
- 2022Frequency-driven Imperceptible Adversarial Attack on Semantic Similaritycited by 77
- 2021Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blinkcited by 75
- 2021FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attackcited by 74
- 2019Towards Large yet Imperceptible Adversarial Image Perturbations with Perceptual Color Distancecited by 73
- 2020The Translucent Patch: A Physical and Universal Attack on Object Detectorscited by 62
- 2021AdvDrop: Adversarial Attack to DNNs by Dropping Informationcited by 56
- 2021Fooling thermal infrared pedestrian detectors in real world using small bulbscited by 51
- 2022DTA: Physical Camouflage Attacks using Differentiable Transformation Networkcited by 51
- 2022Improving the Transferability of Targeted Adversarial Examples through Object-Based Diverse Inputcited by 50
- 2022Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real Worldcited by 42
- 2023Physically Adversarial Infrared Patches with Learnable Shapes and Locationscited by 39
- 2022Query-Efficient Black-box Adversarial Attacks Guided by a Transfer-based Priorcited by 38
- 2021TnT Attacks! Universal Naturalistic Adversarial Patches Against Deep Neural Network Systemscited by 36
- 2022Physical Backdoor Attacks to Lane Detection Systems in Autonomous Drivingcited by 31
- 2022HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable Designcited by 27
- 2019Amora: Black-box Adversarial Morphing Attackcited by 17
- 2020Adversarial Rain Attack and Defensive Deraining for DNN Perceptioncited by 10
- 2023AdvRain: Adversarial Raindrops to Attack Camera-based Smart Vision Systemscited by 8
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Citing papers per year: 2023: 3; 2024: 1; 2025: 1; 2026: 6.
11 papers.
- 2023Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlookcited by 20
- 2023Adversarial Examples in the Physical World: A Surveycited by 10
- 2023Adversarial Infrared Curves: An Attack on Infrared Pedestrian Detectors in the Physical Worldcited by 10
- 2024Real-world Adversarial Defense against Patch Attacks based on Diffusion Modelcited by 5
- 2025Thermally Activated Dual-Modal Adversarial Clothing against AI Surveillance Systemscited by 2
- 2026Knowledge-Guided Adversarial Training for Infrared Object Detection via Thermal Radiation Modelingcited by 1
- 2026Thermal Topology Collapse: Universal Physical Patch Attacks on Infrared Vision Systemscited by 0
- 2026Revealing Physical-World Semantic Vulnerabilities: Universal Adversarial Patch for Infrared Vision-Language Modelscited by 0
- 2026Physical Adversarial Attacks on AI Surveillance Systems:Detection, Tracking, and Visible--Infrared Evasioncited by 0
- 2026InfraQR: Edge-Placed QR-Inspired Structured Patch Attacks on Infrared Vision-Language Modelscited by 0
- 2026Physical Adversarial Examples for Person Detectors in Thermal Images Based on 3D Modelingcited by 0
- 2026Physical Adversarial Examples for Person Detectors in Thermal Images Based on 3D Modeling31 Aug 2026
- 2026InfraQR: Edge-Placed QR-Inspired Structured Patch Attacks on Infrared Vision-Language Models8 Jul 2026
- 2026Physical Adversarial Attacks on AI Surveillance Systems:Detection, Tracking, and Visible--Infrared Evasion8 Apr 2026
- 2026Revealing Physical-World Semantic Vulnerabilities: Universal Adversarial Patch for Infrared Vision-Language Models3 Apr 2026
- 2026Knowledge-Guided Adversarial Training for Infrared Object Detection via Thermal Radiation Modeling26 Mar 2026
- 2026Thermal Topology Collapse: Universal Physical Patch Attacks on Infrared Vision Systems23 Mar 2026
- 2025Thermally Activated Dual-Modal Adversarial Clothing against AI Surveillance Systems13 Nov 2025
- 2024Real-world Adversarial Defense against Patch Attacks based on Diffusion Model14 Sep 2024
- 2023Adversarial Infrared Curves: An Attack on Infrared Pedestrian Detectors in the Physical World21 Dec 2023
- 2023Adversarial Examples in the Physical World: A Survey1 Nov 2023
- 2023Physical Adversarial Attacks For Camera-based Smart Systems: Current Trends, Categorization, Applications, Research Challenges, and Future Outlook11 Aug 2023