mlsanityData to 14 Oct 2026

Two-stage optimized unified adversarial patch for attacking visible-infrared cross-modal detectors in the physical world

arXiv 2312.01789 ↗First posted 4 Dec 2023cs.CVcited by 4

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Abstract (arXiv)

Currently, many studies have addressed security concerns related to visible and infrared detectors independently. In practical scenarios, utilizing cross-modal detectors for tasks proves more reliable than relying on single-modal detectors. Despite this, there is a lack of comprehensive security evaluations for cross-modal detectors. While existing research has explored the feasibility of attacks against cross-modal detectors, the implementation of a robust attack remains unaddressed. This work introduces the Two-stage Optimized Unified Adversarial Patch (TOUAP) designed for performing attacks against visible-infrared cross-modal detectors in real-world, black-box settings. The TOUAP employs a two-stage optimization process: firstly, PSO optimizes an irregular polygonal infrared patch to attack the infrared detector; secondly, the color QR code is optimized, and the shape information of the infrared patch from the first stage is used as a mask. The resulting irregular polygon visible modal patch executes an attack on the visible detector. Through extensive experiments conducted in both digital and physical environments, we validate the effectiveness and robustness of the proposed method. As the TOUAP surpasses baseline performance, we advocate for its widespread attention.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 30 of its 41 references that are in the map. First posted on arXiv 4 Dec 2023.

Builds on

30 of 41 references.

References · 30

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  8. 2022Target-aware Dual Adversarial Learning and a Multi-scenario Multi-Modality Benchmark to Fuse Infrared and Visible for Object Detectioncited by 317
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  11. 2021LLVIP: A Visible-infrared Paired Dataset for Low-light Visioncited by 231
  12. 2019Making an Invisibility Cloak: Real World Adversarial Attacks on Object Detectorscited by 155
18 more
  1. 2021Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical Worldcited by 127
  2. 2021You Only Look One-level Featurecited by 116
  3. 2022ReSTR: Convolution-free Referring Image Segmentation Using Transformerscited by 112
  4. 2022Adversarial Texture for Fooling Person Detectors in the Physical Worldcited by 96
  5. 2021Adversarial Sticker: A Stealthy Attack Method in the Physical Worldcited by 93
  6. 2019Universal Physical Camouflage Attacks on Object Detectorscited by 91
  7. 2022Unsupervised Misaligned Infrared and Visible Image Fusion via Cross-Modality Image Generation and Registrationcited by 80
  8. 2021FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attackcited by 74
  9. 2020The Translucent Patch: A Physical and Universal Attack on Object Detectorscited by 62
  10. 2021AdvDrop: Adversarial Attack to DNNs by Dropping Informationcited by 56
  11. 2021Fooling thermal infrared pedestrian detectors in real world using small bulbscited by 51
  12. 2021Exploring Adversarial Robustness of Multi-Sensor Perception Systems in Self Drivingcited by 45
  13. 2022Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real Worldcited by 42
  14. 2023Physically Adversarial Infrared Patches with Learnable Shapes and Locationscited by 39
  15. 2022HOTCOLD Block: Fooling Thermal Infrared Detectors with a Novel Wearable Designcited by 27
  16. 2023Unified Adversarial Patch for Cross-modal Attacks in the Physical Worldcited by 27
  17. 2022Adversarial Laser Spot: Robust and Covert Physical-World Attack to DNNscited by 11
  18. 2022Adversarial Neon Beam: A Light-based Physical Attack to DNNscited by 6

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025Thermally Activated Dual-Modal Adversarial Clothing against AI Surveillance Systemscited by 2
  2. 2026Transferable Physical-World Adversarial Patches Against Object Detection in Autonomous Drivingcited by 1
  3. 2026Exposing Vulnerabilities in Visible-Infrared VLMs: A Unified Geometric Adversarial Framework with Cross-Task Transferabilitycited by 0
  4. 2026Stealthy in Semantics, Antagonistic in Space: Attacking Visible-Infrared Object Detectors via Object-Level Misalignmentcited by 0