# EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAV

arXiv: https://arxiv.org/abs/2403.05422 · HTML: https://mlsanity.com/paper/2403.05422/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 46 references that are in the map.
- First posted on arXiv 8 Mar 2024.

## Builds on (most cited first, 20 of 20)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Relation Networks for Object Detection](https://mlsanity.com/paper/1711.11575/) (2017)
- [Adversarial Examples for Semantic Segmentation and Object Detection](https://mlsanity.com/paper/1703.08603/) (2017)
- [Fooling automated surveillance cameras: adversarial patches to attack person detection](https://mlsanity.com/paper/1904.08653/) (2019)
- [The Unmanned Aerial Vehicle Benchmark: Object Detection and Tracking](https://mlsanity.com/paper/1804.00518/) (2018)
- [V2V4Real: A Real-world Large-scale Dataset for Vehicle-to-Vehicle Cooperative Perception](https://mlsanity.com/paper/2303.07601/) (2023)
- [DPatch: An Adversarial Patch Attack on Object Detectors](https://mlsanity.com/paper/1806.02299/) (2018)
- [Autonomous Vehicles on the Edge: A Survey on Autonomous Vehicle Racing](https://mlsanity.com/paper/2202.07008/) (2022)
- [Unmanned Aerial Vehicles: A Survey on Civil Applications and Key Research Challenges](https://mlsanity.com/paper/1805.00881/) (2018)
- [Wireless Communications with Unmanned Aerial Vehicles: Opportunities and Challenges](https://mlsanity.com/paper/1602.03602/) (2016)
- [Domain Adaptive Object Detection for Autonomous Driving under Foggy Weather](https://mlsanity.com/paper/2210.15176/) (2022)
- [Adversarial Attacks on Face Detectors using Neural Net based Constrained Optimization](https://mlsanity.com/paper/1805.12302/) (2018)
- [Robust Adversarial Attacks Detection based on Explainable Deep Reinforcement Learning For UAV Guidance and Planning](https://mlsanity.com/paper/2206.02670/) (2022)
- [Exploring the Vulnerability of Single Shot Module in Object Detectors via Imperceptible Background Patches](https://mlsanity.com/paper/1809.05966/) (2018)
- [Deep Transfer Learning for Intelligent Vehicle Perception: a Survey](https://mlsanity.com/paper/2306.15110/) (2023)
- [Defense against Adversarial Cloud Attack on Remote Sensing Salient Object Detection](https://mlsanity.com/paper/2306.17431/) (2023)
- [Coarse-to-fine Task-driven Inpainting for Geoscience Images](https://mlsanity.com/paper/2211.11059/) (2022)

## Built on by (most cited first, 2 of 2)

- [Towards Building Secure UAV Navigation with FHE-aware Knowledge Distillation](https://mlsanity.com/paper/2411.00403/) (2024)
- [Texture- and Shape-based Adversarial Attacks for Overhead Image Vehicle Detection](https://mlsanity.com/paper/2412.16358/) (2024)

## Abstract (arXiv)

Vehicle detection in Unmanned Aerial Vehicle (UAV) captured images has wide applications in aerial photography and remote sensing. There are many public benchmark datasets proposed for the vehicle detection and tracking in UAV images. Recent studies show that adding an adversarial patch on objects can fool the well-trained deep neural networks based object detectors, posing security concerns to the downstream tasks. However, the current public UAV datasets might ignore the diverse altitudes, vehicle attributes, fine-grained instance-level annotation in mostly side view with blurred vehicle roof, so none of them is good to study the adversarial patch based vehicle detection attack problem. In this paper, we propose a new dataset named EVD4UAV as an altitude-sensitive benchmark to evade vehicle detection in UAV with 6,284 images and 90,886 fine-grained annotated vehicles. The EVD4UAV dataset has diverse altitudes (50m, 70m, 90m), vehicle attributes (color, type), fine-grained annotation (horizontal and rotated bounding boxes, instance-level mask) in top view with clear vehicle roof. One white-box and two black-box patch based attack methods are implemented to attack three classic deep neural networks based object detectors on EVD4UAV. The experimental results show that these representative attack methods could not achieve the robust altitude-insensitive attack performance.