# CDUPatch: Color-Driven Universal Adversarial Patch Attack for Dual-Modal Visible-Infrared Detectors

arXiv: https://arxiv.org/abs/2504.10888 · HTML: https://mlsanity.com/paper/2504.10888/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 41 references that are in the map.
- First posted on arXiv 15 Apr 2025.

## Builds on (most cited first, 25 of 25)

- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [Adversarial Patch](https://mlsanity.com/paper/1712.09665/) (2017)
- [Fooling automated surveillance cameras: adversarial patches to attack person detection](https://mlsanity.com/paper/1904.08653/) (2019)
- [Detection and Tracking Meet Drones Challenge](https://mlsanity.com/paper/2001.06303/) (2020)
- [Adversarial Attacks and Defenses in Images, Graphs and Text: A Review](https://mlsanity.com/paper/1909.08072/) (2019)
- [LLVIP: A Visible-infrared Paired Dataset for Low-light Vision](https://mlsanity.com/paper/2108.10831/) (2021)
- [DPatch: An Adversarial Patch Attack on Object Detectors](https://mlsanity.com/paper/1806.02299/) (2018)
- [Towards Large-Scale Small Object Detection: Survey and Benchmarks](https://mlsanity.com/paper/2207.14096/) (2022)
- [Adversarial Sticker: A Stealthy Attack Method in the Physical World](https://mlsanity.com/paper/2104.06728/) (2021)
- [FCA: Learning a 3D Full-coverage Vehicle Camouflage for Multi-view Physical Adversarial Attack](https://mlsanity.com/paper/2109.07193/) (2021)
- [Physical Adversarial Attack meets Computer Vision: A Decade Survey](https://mlsanity.com/paper/2209.15179/) (2022)
- [T-SEA: Transfer-based Self-Ensemble Attack on Object Detection](https://mlsanity.com/paper/2211.09773/) (2022)
- [Infrared Invisible Clothing:Hiding from Infrared Detectors at Multiple Angles in Real World](https://mlsanity.com/paper/2205.05909/) (2022)
- [Synthesizing Robust Adversarial Examples](https://mlsanity.com/paper/1707.07397/) (2017)
- [Physically Adversarial Infrared Patches with Learnable Shapes and Locations](https://mlsanity.com/paper/2303.13868/) (2023)
- [An Interactively Reinforced Paradigm for Joint Infrared-Visible Image Fusion and Saliency Object Detection](https://mlsanity.com/paper/2305.09999/) (2023)
- [Robust Tracking against Adversarial Attacks](https://mlsanity.com/paper/2007.09919/) (2020)
- [IoU Attack: Towards Temporally Coherent Black-Box Adversarial Attack for Visual Object Tracking](https://mlsanity.com/paper/2103.14938/) (2021)
- [TPatch: A Triggered Physical Adversarial Patch](https://mlsanity.com/paper/2401.00148/) (2023)
- [Unified Adversarial Patch for Visible-Infrared Cross-modal Attacks in the Physical World](https://mlsanity.com/paper/2307.14682/) (2023)
- [When Lighting Deceives: Exposing Vision-Language Models' Illumination Vulnerability Through Illumination Transformation Attack](https://mlsanity.com/paper/2503.06903/) (2025)
- [Omniview-Tuning: Boosting Viewpoint Invariance of Vision-Language Pre-training Models](https://mlsanity.com/paper/2404.12139/) (2024)
- [AdvDreamer Unveils: Are Vision-Language Models Truly Ready for Real-World 3D Variations?](https://mlsanity.com/paper/2412.03002/) (2024)
- [Robust SAM: On the Adversarial Robustness of Vision Foundation Models](https://mlsanity.com/paper/2504.08906/) (2025)

## Built on by (most cited first, 6 of 6)

- [Thermally Activated Dual-Modal Adversarial Clothing against AI Surveillance Systems](https://mlsanity.com/paper/2511.09829/) (2025)
- [Thermal Topology Collapse: Universal Physical Patch Attacks on Infrared Vision Systems](https://mlsanity.com/paper/2603.21876/) (2026)
- [Physical Adversarial Attacks on AI Surveillance Systems:Detection, Tracking, and Visible--Infrared Evasion](https://mlsanity.com/paper/2604.06865/) (2026)
- [Exposing Vulnerabilities in Visible-Infrared VLMs: A Unified Geometric Adversarial Framework with Cross-Task Transferability](https://mlsanity.com/paper/2605.22273/) (2026)
- [AdvTiles: Physical Adversarial Camouflage Clothing against Person Detectors via Learnable Tiles](https://mlsanity.com/paper/2608.06801/) (2026)
- [Stealthy in Semantics, Antagonistic in Space: Attacking Visible-Infrared Object Detectors via Object-Level Misalignment](https://mlsanity.com/paper/2609.18133/) (2026)

## Abstract (arXiv)

Adversarial patches are widely used to evaluate the robustness of object detection systems in real-world scenarios. These patches were initially designed to deceive single-modal detectors (e.g., visible or infrared) and have recently been extended to target visible-infrared dual-modal detectors. However, existing dual-modal adversarial patch attacks have limited attack effectiveness across diverse physical scenarios. To address this, we propose CDUPatch, a universal cross-modal patch attack against visible-infrared object detectors across scales, views, and scenarios. Specifically, we observe that color variations lead to different levels of thermal absorption, resulting in temperature differences in infrared imaging. Leveraging this property, we propose an RGB-to-infrared adapter that maps RGB patches to infrared patches, enabling unified optimization of cross-modal patches. By learning an optimal color distribution on the adversarial patch, we can manipulate its thermal response and generate an adversarial infrared texture. Additionally, we introduce a multi-scale clipping strategy and construct a new visible-infrared dataset, MSDrone, which contains aerial vehicle images in varying scales and perspectives. These data augmentation strategies enhance the robustness of our patch in real-world conditions. Experiments on four benchmark datasets (e.g., DroneVehicle, LLVIP, VisDrone, MSDrone) show that our method outperforms existing patch attacks in the digital domain. Extensive physical tests further confirm strong transferability across scales, views, and scenarios.