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

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arxiv 2205.05909 v1 pith:GLA3LLXF submitted 2022-05-12 cs.CV cs.AI

classification cs.CVcs.AI
keywords patternadversarialclothingcodeinfraredloweredyolov3random
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Forward citations

Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 5 citations worldwide. Full citation record

  1. Revealing Physical-World Semantic Vulnerabilities: Universal Adversarial Patch for Infrared Vision-Language Models

    cs.CV 2026-04 unverdicted novelty 7.0 of 10

    UCGP is a universal physical adversarial patch that compromises cross-modal semantic alignment in IR-VLMs through curved-grid parameterization and representation-space disruption.

  2. A Privacy Enhancing Technique to Evade Detection by Street Video Cameras Without Using Adversarial Accessories

    cs.CV 2025-01 conditional novelty 6.0 of 10

    Pedestrians can lower a detector's confidence by walking through spatial 'blind spots' found from confidence heatmaps, and a location-based threshold can partially counter this.

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