Physics-Based Adversarial Attack on Near-Infrared Human Detector for Nighttime Surveillance Camera Systems
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Abstract (arXiv)
Many surveillance cameras switch between daytime and nighttime modes based on illuminance levels. During the day, the camera records ordinary RGB images through an enabled IR-cut filter. At night, the filter is disabled to capture near-infrared (NIR) light emitted from NIR LEDs typically mounted around the lens. While RGB-based AI algorithm vulnerabilities have been widely reported, the vulnerabilities of NIR-based AI have rarely been investigated. In this paper, we identify fundamental vulnerabilities in NIR-based image understanding caused by color and texture loss due to the intrinsic characteristics of clothes' reflectance and cameras' spectral sensitivity in the NIR range. We further show that the nearly co-located configuration of illuminants and cameras in existing surveillance systems facilitates concealing and fully passive attacks in the physical world. Specifically, we demonstrate how retro-reflective and insulation plastic tapes can manipulate the intensity distribution of NIR images. We showcase an attack on the YOLO-based human detector using binary patterns designed in the digital space (via black-box query and searching) and then physically realized using tapes pasted onto clothes. Our attack highlights significant reliability concerns for nighttime surveillance systems, which are intended to enhance security. Codes Available: https://github.com/MyNiuuu/AdvNIR
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 23 of its 46 references that are in the map. First posted on arXiv 18 Dec 2024.
Builds on
23 of 46 references.
References · 23
- 2019PyTorch: An Imperative Style, High-Performance Deep Learning Librarycited by 20,305
- 2020End-to-End Object Detection with Transformerscited by 7,414
- 2014Explaining and Harnessing Adversarial Examplescited by 7,364
- 2015You Only Look Once: Unified, Real-Time Object Detectioncited by 4,852
- 2016Towards Evaluating the Robustness of Neural Networkscited by 2,998
- 2015FaceNet: A Unified Embedding for Face Recognition and Clusteringcited by 2,849
- 2019Expressive Body Capture: 3D Hands, Face, and Body from a Single Imagecited by 1,612
- 2016Universal adversarial perturbationscited by 784
- 2019Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectorscited by 155
- 2020Adversarial Camouflage: Hiding Physical-World Attacks with Natural Stylescited by 126
- 2022Shadows can be Dangerous: Stealthy and Effective Physical-world Adversarial Attack by Natural Phenomenoncited by 101
- 2021Adversarial Laser Beam: Effective Physical-World Attack to DNNs in a Blinkcited by 75
11 more
- 2021FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attackcited by 74
- 2020The Translucent Patch: A Physical and Universal Attack on Object Detectorscited by 62
- 2021Fooling thermal infrared pedestrian detectors in real world using small bulbscited by 51
- 2022DTA: Physical Camouflage Attacks using Differentiable Transformation Networkcited by 51
- 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
- 2019Heuristic Black-box Adversarial Attacks on Video Recognition Modelscited by 37
- 2021Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformationscited by 28
- 2021BASAR:Black-box Attack on Skeletal Action Recognitioncited by 26
- 2021Learning Coated Adversarial Camouflages for Object Detectorscited by 17
- 2022Adversarial Color Film: Effective Physical-World Attack to DNNscited by 11
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Citing papers per year: 2025: 1; 2026: 2.
3 papers.